Gas turbine dispatch optimizer real-time command and operations

ABSTRACT

A dispatch optimization system leverages ambient and market forecast data as well as asset performance and parts-life models to generate recommended operating schedules for gas turbines or other power-generating plant assets that substantially maximize profit while satisfying parts-life constraints. The system generates operating profiles that balance optimal peak fire opportunities with optimal cold part-load opportunities within a maintenance interval or other operating horizon. During real-time operation of the assets, the optimization system can update the operating schedule based on actual market, ambient, and operating data. The system provides information that can assist operators in determining suitable conditions in which to cold part-load or peak-fire the assets in an optimally profitable manner without violating target life constraints.

TECHNICAL FIELD

The subject matter disclosed herein relates generally to power plantoperation, and, more particularly, to long-term, day ahead, andreal-time operation planning of power-generating plant assets.

BACKGROUND

Many power plants employ gas turbines as a source of power to satisfy atleast part of consumers' overall electrical demand. To ensure healthylong-term operation, plant facility owners typically schedulemaintenance or service for their plant assets at regular intervals.Maintenance intervals for gas turbines are often defined in terms offactored fired hours, whereby maintenance is schedule for a set of gasturbines after the turbines have run for a defined number of factoredfired hours (e.g., 32,000 factored fired hours) since the previousmaintenance activity. The maintenance interval is typically definedbased on an expected parts-life consumption for the gas turbine.

Plant operators sometimes peak-fire their gas turbines above their basecapacity during peak demand periods. Peak-firing gas turbines abovetheir base capacity produces extra power output when needed, but at theexpense of faster parts-life consumption. If gas turbines are peak-firedoften within the maintenance interval (or maintenance life), theincremental parts-life consumption may cause the maintenance interval tobe shortened. As a result, maintenance schedules are pulled in and extracustomer service agreement charges may be incurred. Consideration ofthese extra maintenance costs, in terms of more frequent servicing ofthe gas turbines, can lead plant asset owners to exercise peak-fire modemore conservatively than necessary, which may result in missed revenueopportunity.

The above-described deficiencies of gas turbine operations are merelyintended to provide an overview of some of the problems of currenttechnology, and are not intended to be exhaustive. Other problems withthe state of the art, and corresponding benefits of some of the variousnon-limiting embodiments described herein, may become further apparentupon review of the following detailed description.

SUMMARY

The following presents a simplified summary of the disclosed subjectmatter in order to provide a basic understanding of some aspects of thevarious embodiments. This summary is not an extensive overview of thevarious embodiments. It is intended neither to identify key or criticalelements of the various embodiments nor to delineate the scope of thevarious embodiments. Its sole purpose is to present some concepts of thedisclosure in a streamlined form as a prelude to the more detaileddescription that is presented later.

One or more embodiments provide a method, comprising receiving, by asystem comprising at least one processor, operating profile data for oneor more power-generating assets defining values of one or more operatingvariables for respective time units of a life cycle; determining, by thesystem for a first subset of the respective time units of the life cyclecorresponding to a first operating mode that produces parts-life creditsrelative to a target life, an amount of parts-life credited relative tothe target life based on the operating profile data and parts-life modeldata for the one or more power-generating assets; determining, by thesystem for a second subset of the respective time units of the lifecycle corresponding to a second operating mode that consumes theparts-life credits relative to the target life, an amount of parts-lifeconsumed relative to the target life based on the operating profile dataand the parts-life model data; determining, by the system, an amount ofbanked parts-life at a current time of the life cycle based on a net ofthe amount of parts-life credited and the amount of parts-life consumed;converting, by the system, the amount of banked parts-life to an amountof available power output capable of being generated by the secondoperating mode during the life cycle without violating the target life;and rendering, by the system, the amount of available power output on aninterface display.

Also, In one or more embodiments, a system is provided, comprising aprofile generation component configured to generate operating profiledata for the one or more power-generating assets, wherein the operatingprofile data comprises values of one or more operating variables forrespective time units of a maintenance interval, a parts-life metriccomponent configured to determine, for a first subset of the respectivetime units of the maintenance interval corresponding to a first mode ofoperation that produces parts-life credits relative to a target lifebased on the operating profile data and parts-life model data for theone or more power-generating assets, a number of parts-life creditsgenerated by the first mode of operation, wherein the parts-life creditsrepresent an amount of parts-life that can be consumed by a second modeof operation that consumes the parts-life credits during the maintenanceinterval without violating a constraint relative to a target life;determine, for a second subset of the respective time units of themaintenance interval corresponding to the second mode of operation basedon the operating profile data and the parts-life model data, a number ofparts-life debits generated by the second mode of operation, wherein theparts-life debits represent an amount of parts-life to be compensatedfor by the first mode of operation during the maintenance interval toprevent violation of the constraint relative to the target life,determine an amount of banked parts-life at a current time of themaintenance interval based on a balance of the number of parts-lifecredits and the number of parts-life debits, and convert the amount ofbanked parts-life to an amount of banked power output available for thesecond mode of operation during the maintenance interval that will notviolate the constraint relative to the target life; and a user interfacecomponent configured to render the amount of banked power outputavailable for the second mode of operation on an interface display.

Also, according to one or more embodiments, a non-transitorycomputer-readable medium is provided having stored thereon instructionsthat, in response to execution, cause a safety relay device to performoperations, the operations comprising receiving operating profile datafor one or more power-generating assets defining values of one or moreoperating variables for respective time units of a maintenance interval;determining, for a first subset of the respective time units of themaintenance interval corresponding to a first operating mode thatcredits parts-life relative to a target life for the one or morepower-generating assets, an amount of parts-life credited relative tothe target life based on the operating profile data and parts-life modeldata for the one or more power-generating assets; determining, for asecond subset of the respective time units of the maintenance intervalcorresponding to a second operating mode that consumes parts-liferelative to the target life for the one or more power-generating assets,an amount of parts-life consumed relative to the target life based onthe operating profile data and the parts-life model data; determining anamount of banked parts-life at a current time of the maintenanceinterval based on a net of the amount of parts-life credited and theamount of parts-life consumed; determining an amount of available poweroutput capable of being generated by the second operating mode during aremainder of the maintenance interval without violating the target lifeas a function of the amount of banked parts-life; and displaying theamount of available power output on an interface display.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages, and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the drawings. It will also be appreciatedthat the detailed description may include additional or alternativeembodiments beyond those described in this summary.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example 2×1 combined cycle plantcomprising two gas turbines.

FIG. 2 is an example graph of a steady state efficiency model for thecombined cycle plant.

FIG. 3 is a generalized graph illustrating this tradeoff between assetlife and fuel efficiency.

FIG. 4 is a block diagram of an example dispatch optimization system forgas turbines or other plant assets.

FIG. 5 is a graph plotting the change in parts-life for a gas turbine ora set of gas turbines as a function of time over a total maintenanceinterval for an example operating scenario.

FIG. 6 is an example overview display for the dispatch optimizationsystem.

FIG. 7 is a block diagram illustrating example data inputs and outputsfor a profile generation component of the dispatch optimization systemduring the long-term planning operations.

FIG. 8 illustrates example tabular formats for pre-stored model datavalues.

FIG. 9 depicts graphs that plot example forecast data under threecategories—predicted market conditions, predicted ambient conditions,and predicted load or electrical demand.

FIG. 10 is a computational block diagram illustrating an iterativeanalysis performed on forecast data and model data by a profilegeneration component to produce a substantially optimized operatingprofile.

FIG. 11 is an example display format for an operating profile.

FIG. 12 is an example graphic that plots recommended operatingtemperature defined by an operating profile over the duration of amaintenance interval together with predicted hourly electricity pricesover the same maintenance interval.

FIG. 13 is an example graphic display that can be generated by a userinterface component based on results of long-term analysis.

FIG. 14 is an example graphical display that can be generated by a userinterface component.

FIG. 15 is a block diagram illustrating example data inputs and outputsfor a profile generation component of the dispatch optimization systemduring day-ahead and real-time planning and execution.

FIG. 16 is an example display screen that presents day-ahead capacityinformation generated by the dispatch optimization system.

FIG. 17 is an example display screen that allows a user to enterday-ahead cleared bid and operating information for the next operatingday.

FIG. 18 is an example day-ahead planning display screen.

FIG. 19 is an example real-time execution display screen that can begenerated by a user interface component of the dispatch optimizationsystem.

FIG. 20 is an example real-time monitoring display screen that can begenerated by a user interface component of the dispatch optimizationsystem.

FIG. 21 is a flowchart of an example methodology for generating aprofit-maximizing operating schedule or profile for a plant asset.

FIG. 22 is a flowchart of an example methodology for determiningparts-life credits and deficits relative to a target life for one ormore power-generating plant assets.

FIG. 23 is a flowchart of an example methodology for identifyingsuitable periods during which to profitably peak-fire power-generatingplant assets.

FIG. 24 is a flowchart of an example methodology for identifyingsuitable periods during which to cold part-load power-generating plantassets in a cost-efficient manner.

FIG. 25 is an example computing environment.

FIG. 26 is an example networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawingswherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

As used in the subject specification and drawings, the terms “object,”“module,” “interface,” “component,” “system,” “platform,” “engine,”“selector,” “manager,” “unit,” “store,” “network,” “generator” and thelike are intended to refer to a computer-related entity or an entityrelated to, or that is part of, an operational machine or apparatus witha specific functionality; such entities can be either hardware, acombination of hardware and firmware, firmware, a combination ofhardware and software, software, or software in execution. In addition,entities identified through the foregoing terms are herein genericallyreferred to as “functional elements.” As an example, a component can be,but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentsmay reside within a process and/or thread of execution and a componentmay be localized on one computer and/or distributed between two or morecomputers. Also, these components can execute from variouscomputer-readable storage media having various data structures storedthereon. The components may communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As an example,a component can be an apparatus with specific functionality provided bymechanical parts operated by electric or electronic circuitry, which isoperated by software, or firmware application executed by a processor,wherein the processor can be internal or external to the apparatus andexecutes at least a part of the software or firmware application. Asanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. Interface(s) can include input/output (I/O)components as well as associated processor(s), application(s), or API(Application Program Interface) component(s). While examples presentedhereinabove are directed to a component, the exemplified features oraspects also apply to object, module, interface, system, platform,engine, selector, manager, unit, store, network, and the like.

Power-generating plant assets, such as gas turbines, require regularmaintenance to ensure safe, reliable, and efficient operation. Manyplant asset owners contract the manufacturers of such plant assets toperform scheduled maintenance on the assets. In some cases, customerservice agreements (CSAs) define a usage-based maintenance schemewhereby the manufacturer guarantees an operational duration for a givenasset when subject to periodic maintenance. Under such contracts, theasset owner may pay the manufacturer a fee (e.g., a usage-based fee) formaintenance of the asset, and the manufacturer performs routineinspections, provides for repairs and part replacements when necessary,and performs other service functions required to keep the plant assetfunctioning within the agreed upon duration (or asset life). Since thecosts of such repairs and replacements, as well as the frequency ofinspections, depend on the operating history of the asset, themanufacturer and owner may agree upon a system of determining theoperational impact on the useful life of the asset in connection withdetermining when maintenance should be performed.

One way of accounting for the operational impact on the asset is bytracking a parts-life metric called factored fired hours (FFHs).According to this approach, in an example applied to a gas turbine,every hour that the gas turbine is operated (or “fired”) up to its basecapacity (that is, the rated capacity or base load of the gas turbine),one FFH is accrued for the gas turbine. An example gas turbine maycomprise components designed to run for 32,000 FFH. If the gas turbineis operated up to its base capacity, each actual hour of operationtranslates to one FFH.

Gas turbines can also be operated in peak-fire mode, whereby the firingtemperature (or operating temperature) is raised above the design valuein order to produce a higher megawatt (MW) output relative to base-loadoperation. However, peak-firing also accelerates parts-life consumption.To reflect the faster consumption of parts-life during peak-firing, thenumber of FFH per hour of actual peak-fire operation becomes greaterthan one (e.g., 1 hour of peak-fire operation=2.2 FFH). This simplifiedFFH model is only intended to be exemplary; in some cases the actualfunctional relationship between gas turbine operation and FHH consumedmay be based on a detailed assessment of physics, risk analysis, orother factors.

According to this FFH approach, a gas turbine that only operates up toits base load will consume its 32,000 FFH parts-life (or maintenanceinterval) in 32,000 actual hours of operation, whereas a gas turbinethat operates in peak-fire mode for at least some of its maintenanceinterval will consume its parts-life in fewer than 32,000 hours ofactual operation, resulting in a shortened maintenance interval.

Some model-based controls allow plant operators to compensate forincremental parts-life consumption resulting from peak-firing periods bycold part-loading (CPL) the gas turbines during other periods. Runninggas turbines in CPL mode is less fuel efficient than default operation,but can beneficially extend asset parts-life (in terms of factored firedhours or another parts-life metric), thereby at least partiallycompensating for the extra parts-life consumed during peak-firingperiods. Thus, there is a tradeoff between fuel efficiency andparts-life.

FIG. 1 is a block diagram of an example 2×1 combined cycle plant 102,comprising two gas turbines 104 ₁ and 104 ₂, a heat recovery steamgenerator 106, and a steam turbine 108. FIG. 2 is an example graph 202of a steady state efficiency model for the combined cycle plant 102.Graph 202 plots the gas turbine operating temperature (represented bythe exhaust temperature in this example) as a function of the plant'spower output for both hot load operation (line 204) and cold part-loadoperation (line 206). Between the minimum plant power output and basecapacity (non-peak-fire operation), hot load operation—whereby the gasturbines generate power at a higher temperature, as represented by line204—results in highest fuel efficiency in combined cycle operations, andis therefore the preferred operating mode for base operation in thisscenario. If power output stays below the plant's base capacity of theplant 102 and the gas turbines are operated in hot load mode, the FFHthat define the maintenance interval (or life cycle) for the gasturbines are equal to the actual operating hours.

During peak-firing (or over-firing), whereby the plant 102 generatespower above the base capacity (up to the plant's maximum capacity),additional megawatts are generated, but at the cost of faster partdegradation (parts-life consumption). Some systems adjust themaintenance interval for the plant assets to account for thisaccelerated parts-life consumption by adjusting the actual number ofaccumulated operating hours for the assets to yield the FFH metric,which is used to determine when maintenance should next be performed onthe assets (e.g., when the factored fired hours reaches 32,000 hours).Since peak-firing consumes parts-life at an accelerated rate, FFH areconsumed faster during peak-firing relative to hot load operation belowthe base capacity. If these consumed FFH are not compensated for withinthe maintenance interval, the total maintenance interval (or operatinghorizon) will be shortened, necessitating more frequent maintenance anduncertainty in planned outage dates.

To compensate for FFH consumed during peak firing periods, the plant 102can be operated in cold part-load (CPL) mode (represented by line 206)during selected non-peak periods. During CPL operation, the gas turbinesare operated at lower temperatures relative to hot loading. While CPLoperation is less fuel efficient than hot loading, running gas turbinesin CPL mode can slow part degradation and extend the maintenance life ofthe turbines. FIG. 3 is a generalized graph 302 illustrating thistradeoff between asset life and fuel efficiency. Point 304 of graph 302represents hot load operation (non-peak) and point 306 represents CPLoperation. As demonstrated by this graph, higher operating temperaturesyield more efficient operation with a shorter parts-life (and acorrespondingly shorter maintenance interval), while cooler operatingtemperatures can extend parts-life at the expense of fuel efficiency.

Thus, whereas peak-firing “consumes” factored fired hours at a fasterrate relative to normal hot load operation, CPL operation can “make”factored fired hours, thereby compensating for the extra FFH consumedduring peak firing (though at the expense of fuel efficiency). Ingeneral, CPL operation creates FFH credits, while peak firing consumesor exhausts these FFH credits (although examples described herein assumethat the maintenance interval is defined in terms of FFH, it is to beunderstood that the maintenance interval may be defined in terms of adifferent parts-life metric in some systems).

Different costs can be attributed to these operational tradeoffs. Thelower fuel efficiency associated with running the gas turbinesexcessively in CPL mode can increase fuel costs, whereas excessivepeak-firing can increase maintenance costs (e.g., customer servicemaintenance costs) by shortening the maintenance intervals andnecessitating more frequent servicing of plant assets. The flexibilityinherent in plant asset operation offers plant operators a range ofoperating choices at any given moment, ranging from most fuel efficient(default operation) to operation that minimizes parts-life consumption(CPL operation). Embodiments of the systems and methods described hereinexploit this flexibility to “save” parts-life during CPL operatingperiods, and to “consume” the saved parts-life during peak-fireoperations to produce extra power (MWs) over base capacity at othertimes (thereby compensating for the detrimental effects of peak-fireconsumption).

Ideally, profits associated with power production could be substantiallymaximized if an optimal balance between under-firing and over-firing ofthe gas turbines across the maintenance interval can be identified.However, finding this substantially optimized balance is rendereddifficult due in part to the large number of variable factors to beconsidered, including hourly fuel costs, electricity costs, and expectedelectrical demand or load, all of which vary as a function of timeacross the maintenance interval. The detrimental effects of uncertaintyand reduction in operating hours (e.g., uncertainty associated withmaintenance planning) can discourage owners from peak-firing theirassets. As a result, power-generating plant assets are utilized belowtheir value potential, resulting in loss of potential profit associatedwith missed opportunities for peak-firing.

To address these and other issues, one or more embodiments of thepresent disclosure provide systems and methods that substantiallymaximize the untapped value generated by CPL-compensated peak-firing byoptimally balancing the most favorable peak-fire opportunities with themost favorable CPL opportunities. To this end, a dispatch optimizationsystem can leverage forecast information and asset performance modeldata to determine how much parts-life credit (e.g., FFH credit) toaccrue, the best times or conditions to accrue the parts-life credit,and the best times or conditions to consume the accrued parts-lifecredit by peak-firing and producing revenue. In some embodiments, thedispatch optimization system generates an operating profile for one ormore gas turbines (or other power-generating assets) that balances thecreation and consumption of parts-life credits across the scheduledmaintenance interval so that the maintenance interval will not beshortened and additional maintenance costs (e.g., extra customer serviceagreement charges) will not be accrued. The dispatch optimization systemalso determines optimal times for creating parts-life credits (by coldpart-loading) and consuming the parts-life credits (by peak-firing) thatsubstantially maximize profit given projected energy prices, fuel costs,and demand.

The dispatch optimization system also generates and presents informationthat can assist plant operators or managers in making long-term,day-ahead, and same-day asset operating decisions. For example, in someembodiments the dispatch optimization system can convey the value andquality of parts-life used given current or forecasted ambient andmarket conditions. In an example scenario, the dispatch optimizationsystem can determine a long-term price of life value based on forecastedconditions as well as asset performance and parts-life models, andconvert this long-term optimal price of life to actionablerecommendations regarding operating temperature suppression (as afunction of the current operation and ambient conditions).

In various embodiments, long-term and real-time operating profilesgenerated by the optimization system can be leveraged in eitherautomatic or manual asset control strategies. For example, operatingprofiles generated by the system can be exported to a plant assetcontrol system, which can automatically regulate operation of the plantassets in accordance with the profiles. Alternatively, the operatingprofile information can be rendered in a graphical or text-based formatthat can be used as a guideline for operating the gas turbines over themaintenance interval.

Although examples described herein relate to the use of cold part-loadoperation as a means of banking or crediting parts-life and peak-fireoperation as a means of consuming CPL-compensated parts-life, it is tobe appreciated that some embodiments of the systems and methodsdescribed herein can perform the analyses with respect to otheroperating modes that bank and consume parts-life. For example, ratherthan or in addition to determining profitable trade-offs between CPLoperation and peak-fire operation, some embodiments can be configured todetermine profitable trade-offs between high and low amounts of coolingflow in a gas turbine, or between high and low steam inlet temperaturesin a steam turbine. In general, embodiments described herein can beconfigured to determine optimized trade-offs between a “harsher”operation of plant assets and a “gentler” operation of the assets over atime horizon given parts-life constraints.

As illustrated in FIG. 2, the physics of combined cycle operations ofgas turbines (or certain other power-producing plant assets) offer acertain operating flexibility. In particular, the same combined cyclemegawatt output can be generated by either running the gas turbinehotter (which is more fuel efficient and has a nominal impact on life)or running the gas turbine cooler (which is less fuel efficient but hasa lower impact on life). This flexibility is available below the baseload (also called part-load). In general, the dispatch optimizationsystem described herein assists owners of power-generating plant assets(e.g., gas turbines or other power producing assets) to exploit thisflexibility of asset operation in order to offset, manage, and controlimpact on the operation horizon (or maintenance interval) while allowingfor peak-firing, thereby unlocking latent asset value. By varying theextent of cold part-load (CPL) operation, varying levels of parts-lifecredits can be accumulated. These accumulated parts-life credits canthen be used to offset the increased parts-life consumed duringpeak-firing periods. The dispatch optimization system described hereindetermines most favorable conditions for CPL operation, as well asdegrees of CPL operation, in order to maximize asset utilization whilekeeping the operating horizon at a specified duration.

The system can begin by generating a long-term operating profile for theplant assets based on predicted ambient and market conditions. Duringreal-time operation of the asset during the maintenance interval, thesystem can update the operating profile based on actual ambient andmarket conditions as well as the actual operating history of the assetswithin the maintenance interval thus far.

FIG. 4 is a block diagram of an example dispatch optimization system forgas turbines (or other plant assets) according to one or moreembodiments of this disclosure. Aspects of the systems, apparatuses, orprocesses explained in this disclosure can constitute machine-executablecomponents embodied within machine(s), e.g., embodied in one or morecomputer-readable mediums (or media) associated with one or moremachines. Such components, when executed by one or more machines, e.g.,computer(s), computing device(s), automation device(s), virtualmachine(s), etc., can cause the machine(s) to perform the operationsdescribed.

Dispatch optimization system 402 can include a forecasting component404, a profile generation component 406, a user interface component 408,a real-time data acquisition component 410, a parts-life metriccomponent 412, a control interface component 414, one or more processors418, and memory 420. In various embodiments, one or more of theforecasting component 404, profile generation component 406, userinterface component 408, real-time data acquisition component 410,parts-life metric component 412, control interface component 414, theone or more processors 418, and memory 420 can be electrically and/orcommunicatively coupled to one another to perform one or more of thefunctions of the dispatch optimization system 402. In some embodiments,one or more of components 404, 406, 408, 410, 412, and 414 can comprisesoftware instructions stored on memory 420 and executed by processor(s)418. Dispatch optimization system 402 may also interact with otherhardware and/or software components not depicted in FIG. 4. For example,processor(s) 418 may interact with one or more external user interfacedevices, such as a keyboard, a mouse, a display monitor, a touchscreen,or other such interface devices.

Forecasting component 404 can be configured to receive and/or generateforecast data to be used as one or more parameters for generating asubstantially optimized operating profile or schedule for apower-generating plant asset (e.g., one or more gas turbines or othersuch assets). The forecast data can represent predicted conditionsacross the duration of a maintenance interval for which the operatingprofile is being generated. These predicted conditions can include, butare not limited to, electrical demand or load, ambient conditions (e.g.,temperature, pressure, humidity, etc.), electricity prices, and/or gasprices. In some embodiments, the forecast data can be formatted ashourly data. However, other time units for the hourly data are alsowithin the scope of one or more embodiments. In general, the time basefor the forecast data will match the time base for the operatingprofile. Moreover, in addition to or as an alternative to time-seriesdata, some embodiments can be configured to consider market conditionsand ambient conditions in statistical representations—such as histogramsor probability distributions—rather than as time-series data.

Profile generation component 406 can be configured to determine asuitable plant asset operating profile for the maintenance interval,given the forecast data, that substantially maximizes profits andmaintains the specified parts-life target life for the plant assets. Theprofile generation component 406 generates the operating profile basedon the forecast data, performance models for the one or more plantassets being assessed, and a calculated “price of life” valuerepresenting a monetary value of parts-life credited or consumed. In thecase of systems that measure parts-life in terms of factored firedhours, the price of life will have a unit of $/FFH. As will be discussedin more detail below, this price of life estimate can reduce thecomputational burden associated with determining the optimized operatingprofile. In some embodiments, the operating profile can be generated asan hourly operating schedule defining one or both of a power output andan operating temperature for the power-generating plant assets for eachhour of the maintenance interval (although the examples described hereinassume an hourly time base, other time bases for the operating profileare also within the scope of one or more embodiments). The profilegeneration component 406 can also output the price of life estimatedetermined for the maintenance interval.

The user interface component 408 can be configured to receive user inputand to render output to the user in any suitable format (e.g., visual,audio, tactile, etc.). In some embodiments, user interface component 408can be configured to generate a graphical user interface that can berendered on a client device that communicatively interfaces with thedispatch optimization system 402, or on a native display component ofthe system 402 (e.g., a display monitor or screen). Input data caninclude, for example, user-defined constraints to be taken into accountwhen generating an operating profile (e.g., upper and lower limits ongas turbine operating temperature or power output, definition of adesired operating horizon, identification of days during which the gasturbines are not allowed to run, etc.), updated ambient or marketforecast data, plant shutdown date information, asset maintenance dateinformation, or other such information. Output data can include, forexample, a text-based or graphical rendering of a plant asset operatingprofile or schedule; current price of life estimates representing a costof saving additional parts-life given current and predicted conditions;recommended hours of cold part-load and peak-fire operating, estimatesof parts-life remaining based on a net of CPL and peak-fire operations;estimates of the lowest electricity price, given forecasted conditionsand historical operation, that would justify peak-fire operation from aprofit standpoint, a number of banked megawatt-hours (MWh) that can begenerated by peak-fire operation without violating a target lifeconstraint; comparative metric graphs; or other such outputs.

The real-time data acquisition component 410 can be configured toreceive or acquire current or historical values for the ambient, market,and asset operating conditions represented by the forecast data. As willbe described in more detail herein, the optimization system 402 can usethese real-time or historical values to update the recommended operatingprofile for the plant assets during the maintenance interval oroperating horizon, as well as to update parts-life and monetary metricspresented to the user to guide profitable and balanced operation of theplant assets.

The parts-life metric component 412 can be configured to generatevarious metrics relating to parts-life consumed or saved by the plantasset during operation within a maintenance interval, estimated cost ofparts-life saved by CPL operation, banked MWh available for peak-fireoperation as a result of CPL operation, minimum electricity prices atwhich banked MWh can be advantageously generated via peak-fireoperation, or other such metrics. The control interface component 414can be configured to interface and exchange data with a plant assetcontrol system. This can include, for example, exporting operatingprofile information (e.g., hourly power output schedule, hourlyoperating temperatures, etc.) to the control system, and receivingactual real-time and/or historical operating information from thecontrol system for use in updating planning metrics and operatingprofiles.

The one or more processors 418 can perform one or more of the functionsdescribed herein with reference to the systems and/or methods disclosed.Memory 420 can be a computer-readable storage medium storingcomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed.

Although features of the dispatch optimization system 402 are describedherein with reference to gas turbines, it is to be appreciated thatembodiments of the dispatch optimization system 402 are not limited touse with gas turbines, but rather can generate operating profiles orschedules for other types of power-generating assets.

To illustrate the concept of banking and consuming parts-life during aoperation of a plant asset across a maintenance interval, FIG. 5 is agraph 502 plotting the change in parts-life for a gas turbine or a setof gas turbines as a function of time over a total maintenance intervalfor an example operating scenario. The vertical dashed line 508represents the target life for the plant asset, or the time at which thenext maintenance service will be scheduled if no peak-firing or CPLoperation is performed during the maintenance interval. This target lifemay be defined in terms of a number of factored fired hours (e.g.,32,000 factored fired hours), or another parts-life metric. If the gasturbines are only operated to output power up to the base capacity inhot load mode throughout the maintenance interval (referred to asbaseline operation), the target life will be reached when the number ofactual operating hours (or fired hours) reaches the scheduled number offactored fired hours defining the target life.

As noted above, when the plant assets are peak-fired, the number ofremaining factored fired hours is adjusted downward relative to baselineoperation to reflect faster parts-life consumption. The negative curves506 of graph 502 represent peak-fire periods, which cause a downwardadjustment to the maintenance interval duration relative to the baseline(i.e., a negative change of life). When the gas turbines are coldpart-loaded, the number of remaining FFH is adjusted upward to reflectslower parts-life consumption relative to baseline operation. Thepositive curves 504 of graph 502 represent CPL operation, which causesan upward adjustment to the maintenance interval duration relative tothe baseline (i.e., a positive change of life).

As demonstrated by graph 502, CPL operation creates FFH credits (or,more generally, parts-life credits), while peak-firing consumes orexhausts these FFH credits. If the “consumed” FFH credits are balancedwith the “made” FFH credits across the maintenance interval (“made” Δlife=“consumed” Δ life), the net parts-life for the maintenance intervalremains unchanged and the maintenance interval will not be shortened(that is, target life 508 will not be pulled in). Depending on suchfactors as the energy demand, fuel prices, and energy prices at eachtime unit (e.g., hour) of the maintenance interval, the overall profitfrom operating the plant assets over the maintenance interval is partlya function of the selected times during which the assets are peak-firedand cold part-loaded.

During a long-term forecasting and planning stage, the dispatchoptimization system 402 described herein can determine a suitableoperating schedule for the plant assets (e.g., gas turbines) thatsubstantially maximizes the profit over the maintenance interval byidentifying the most favorable peak-firing opportunities given load andambient condition forecasts as well as performance model data for theplant assets, and balancing these peak-firing times by identifying mostfavorable opportunities for CPL operation such that the target life forthe assets remains substantially unchanged.

In order to solve this optimization problem with minimal computationalburden despite the long operating horizon, the dispatch optimizationsystem 402 generates this operating profile based on an estimated priceof life value λ, which represents an incremental extra amount of profitfor incremental additional life. In this regard, the dispatchoptimization system 402 accounts for creation (by CPL) and exhaustion(by peak fire operation) of FFH credits by means of computing the costof such FFH credits based on an estimated price of life value λ (inunits of $/FFH). In an example scenario, it may be determined that,based on the cost of fuel for a given hour, one unit of parts-life savedby CPL operation costs $1 in additional fuel due to the reduced fuelefficiency of CPL operation. It may also be determined that one unit ofparts-life lost as a result of peak-firing will produce one extra MW ofpower. It can therefore be assumed that it is worth saving life via CPLoperation only if the electricity price will be greater than $1/MWh,indicating a suitable opportunity to consume the extra parts-life duringa peak-fire operation.

In some embodiments, the price of life λ may be a vector quantity for agiven plant asset. For example, a given plant asset may comprisemultiple stages, where each stage has a different target life (ormaintenance interval). In such scenarios, each stage may have adifferent price of life value, where the set of price of life values forall stages of the asset make up a price of life vector.

If an estimated ideal price of life is known, the problem simplifies todeciding, at each time unit of the maintenance interval (or in the caseof statistical data, each operating condition), the value of the plantoutput (typically in MW) and/or operating temperature (e.g., exhausttemperature, inlet temperature, etc.) that maximizes profit, as givengenerally by:

Profit=(Electricity revenue)−(Cost of Fuel Burn)−(Cost of Life)  (1)

The electricity revenue can be determined based on the product of theelectricity price for the time unit and the power output (e.g., MWh)generated for the time unit. The cost of fuel burn can be determinedbased on the product of the fuel cost for the time unit and the amountof fuel consumed during the time unit (which can be determined based onperformance model data for the plant asset that models the asset's fuelconsumption as a function of one or both of power output and/oroperating temperature). The cost of life can be determined based on theproduct of the estimated price of life λ and the number of FFH consumed(which can be determined based on parts-life model data for the plantasset that models the consumed FFH as a function of one or both of poweroutput and/or operating temperature).

As will be described in more detail below, the dispatch optimizationsystem 402 uses an iterative analysis technique to determine, for eachtime unit (e.g., hour) of the maintenance interval, a recommended output(MW) and/or operating temperature (T) for the plant asset that a maximumprofit for the maintenance interval while satisfying the target lifeconstraint (that is, without significantly altering the duration of themaintenance interval). This iterative technique comprises an inner loopiteration and an outer loop iteration. The inner loop determines anoperating profile that maximizes the profit for each time unit of themaintenance interval according to equation (1) given an estimated priceof life value λ. The outer loop then determines whether the targetparts-life resulting from the operating profile is substantially equalto the actual target parts-life for the plant asset (within a definedtolerance). If this target parts-life constraint is not satisfied, theestimated price of life value λ is adjusted in the appropriatedirection, and the inner loop is re-executed using the adjusted price oflife value. These iterations are repeated until a profit maximizingoperating profile is found that satisfies the target parts-lifeconstraint for the asset (that is, an operating profile that results ina maintenance interval that is substantially equal to the targetmaintenance interval with a defined tolerance).

FIG. 6 is an example overview display 602 for the dispatch optimizationsystem 402, which can be generated by user interface component 408. Theoverview display 602 can include selectable graphics for navigating toother displays of the system 402, categorized according to long-termplanning 604, day-ahead planning 606, and real-time planning andoperations 608. The long-term planning sequence, during which anoptimized operating profile for a future maintenance interval isgenerated, can be initiated and viewed via interactions with long termdisplays accessible via the long-term graphic 604.

FIG. 7 is a block diagram illustrating example data inputs and outputsfor the profile generation component 406 of the dispatch optimizationsystem 402 during the long-term planning stage. As will be described inmore detail herein, similar inputs and outputs are used duringsubsequent day-ahead and real-time planning and operation, but withactual and/or historical ambient, market, and operational data replacingat least some of the forecast data.

In the examples described herein, the plant assets are assumed to be aset of gas turbines. However, it is to be appreciated that theoptimization techniques carried out by embodiments of the dispatchoptimization system 402 are also applicable to other types ofpower-generating plant assets. Moreover, although the examples describedherein assume an operating profile having an hourly time base, othertime bases are also within the scope of one or more embodiments. Also,while the parts-life metric in the following examples is assumed to beFFH, some embodiments of the dispatch optimization system 402 can beconfigured to determine operating profiles based on other parts-lifemetrics.

Profile generation component 406 is configured to execute one or moreoptimization algorithms 702 that perform the inner loop and outer loopiterations described above. In order to accurately calculate the cost offuel burn and the cost of consumed FFH (or another parts-life metric) bythe gas turbines, the dispatch optimization system 402 is provided withmodel data 416, including one or more fuel consumption models and one ormore parts-life models for the gas turbines. These models can becustomized to the particular gas turbines under investigation based onengineering specifications, historical operation data, or other suchinformation.

An example fuel consumption model may define an estimated amount of fuelconsumed by the gas turbines for a given time unit (e.g., an hour) as afunction of the power output MW and the operating temperature T (whichmay be an exhaust temperature, an inlet temperature, or anothertemperature indicative of the overall operating temperature), and/orambient conditions AMB such as ambient temperature, pressure, humidity,etc. Such performance models may be stored on memory 420 associated withthe dispatch optimization system 402, either as a mathematical function(e.g., FuelUsed(MW, T, Amb)) describing the relationship between fuelconsumption and combinations of MW and T for a given Amb, or as a tableof precomputed values that can be accessed by the profile generationcomponent 406 as needed in order to obtain fuel consumption estimationsfor different operating scenarios.

An example parts-life model may define an estimated number of factoredfired hours (or other parts-life metric) consumed for a given time unitas a function of power output MW and operating temperature T, and/orambient conditions Amb such as ambient temperature, pressure, humidity,etc. Similar to the fuel consumption model, the parts-life model can bestored on memory 420, either as a mathematical function (e.g.,FHH_Consumed(MW, T, Amb)) describing the relationship between FFHconsumed and combinations of MW and T for a given Amb, or as a table ofprecomputed values that can be accessed by the profile generationcomponent 406 as needed in order to obtain FHH consumption estimationsfor different operating scenarios.

FIG. 8 illustrates example tabular formats for pre-stored model datavalues for a given Amb. Table 802 is an example data table correspondingto a performance model (e.g., FuelUsed(MW, T)), and table 804 is anexample data table corresponding to a parts-life model (e.g.,FFH_consumed(MW, T)). Performance model data table 802 is atwo-dimensional grid of fuel consumption values 808 for respectivecombinations of power output MW (columns 812) and operating temperatureT (rows 814). For embodiments in which the model data is stored aspre-computed values, the performance model data values can be stored inmemory 420 in a format similar to that shown in table 802 (or anothersuitable format), whereby precomputed data values 808 representing theamount of fuel consumed for a given combination of power output (MW) andoperating temperature (T) are stored for a range of [MW, T] pairs (e.g.,as comma-delimited data or any other suitable storage format). During aniteration of the optimization process to be described in more detailbelow, the profile generation component 406 can access table 702 andretrieve the pre-stored value of FuelUsed(MW, T) corresponding to thepower output and operating temperature value pair that most closelymatch the pair of test values under consideration for the currentiteration of a given hour. Pre-storing these precomputed values canreduce the computational burden of the optimization process relative tostoring the model data as mathematical functions that must be executedwith each iteration in order to compute the amount of fuel consumed fora given [MW, T] pair.

The parts-life model data table 804 can be stored in a similar format.In particular, the parts-life model data table comprises a grid ofvalues 810 representing the number of FFH consumed during a time unit(e.g., an hour) in which the one or more gas turbines output an amountof power MW at an operating temperature of T for a range of values of MWand T. During operation, the profile generation component 406 can accesstable 704 to retrieve the number of FFH consumed for a given [MW, T]pair being considered in a current iteration of the optimizationsequence.

The data values 808 and 810 can be stored at any suitable degree ofgranularity of MW and T values. The degree of granularity may depend,for example, on the constraints of the computational environment onwhich the dispatch optimization system runs, whereby environments withsufficiently large data storage capacity can store the data values 808and 810 at a higher degree of granularity (resulting in a larger numberof pre-computed values). In the example tables 802 and 804 depicted inFIG. 8, the data values 808 and 810 are stored at a granularity of 10 MWand 10° F. However, other suitable granularities are also within thescope of one or more embodiments. If the values of MW and T beingconsidered by the profile generation component 406 fall between theavailable values of MW and/or T represented in tables 802 and 804, theprofile generation component 406 may select values of MW and Trepresented in tables 802 and 804 that are nearest to the values of MWand T being considered (e.g., by rounding the values under considerationto the nearest tabulated values), and select the fuel consumption andFFH values corresponding to these nearest values of MW and T.Alternatively, in some embodiments, the profile generation component 406may be configured to interpolate between the tabulated values 808 and810 if the actual values of MW and T being considered fall betweentabulated values represented in tables 802 and 804.

While FIG. 8 depicts the fuel consumption values and FFH values as beingfunctions solely of power output and operating temperature, someembodiments may model the fuel consumption and FFH consumption asfunctions of additional factors. In such embodiments, the pre-calculateddata values may be stored as higher order tables depending on the numberof variables used to calculated fuel consumption and FFH. Also, whiledeterministic models are assumed in the present example, in someembodiments stochastic models may be used to model the performance andparts-life for a given plant asset.

Returning now to FIG. 7, forecast data 710 provided to the system is nowdescribed. When an operating profile for one or more gas turbines (orother plant assets) is to be generated for an operating interval (e.g.,a maintenance interval), forecast data 710 is leveraged to provide anominal prediction of operations and conditions for the operatinginterval being planned. Forecast data 710 can comprise time-basedinformation (e.g., hourly data) describing expected ambient conditionsand/or market factors that play a role in deciding when to peak-fire thegas turbines and when to operate the gas turbines in CPL mode, where theforecast data 710 encompasses a time range corresponding to theoperating interval being planned. FIG. 9 depicts graphs that plotexample forecast data under three categories—predicted market conditions(graph 902), predicted ambient conditions (graph 904), and predictedload or electrical demand (graph 906). Predicted market conditions caninclude for example, predicted hourly electricity prices and predictedhourly gas prices (or hourly prices for other types of fuel burned bythe plant assets under investigation). Predicted ambient conditions caninclude, for example, predicted hourly temperature, pressure, and/orhumidity. The time range for the forecast data 610 encompasses the hoursthat make up the maintenance interval being planned (e.g., a 32,000 FFHduration in the present example). Although the forecast data 610 isdescribed as having an hourly time base, other time bases may be usedwithout departing from the scope of one or more embodiments describedherein. In general, the time base for the forecast data 610 will matchthe time base used for plant operation (e.g., hourly data for powerplants that participate in day-ahead electricity markets).

Returning now to FIG. 7, in some embodiments, some or all of theforecast data 710 can be entered into the dispatch optimization system402 by the user (e.g., via user interface component 408). Alternatively,forecasting component 404 may be configured to retrieve some or all ofthe forecast data 710 from external sources of forecast information,including but not limited to weather forecasting websites, electricityand/or gas market websites, or other such sources. In yet anotherexample, forecasting component 404 can be configured to generate some orall of the forecast data 710 based on analysis of relevant data setsprovided to the forecasting component 404. For example, in someembodiments, forecasting component 404 can be configured to execute loadforecasting algorithms that generate an expected hourly electricaldemand for the maintenance interval based on forecasted weather orambient conditions for the maintenance interval. In such embodiments,the forecasting component 404 can be provided with such ambientcondition information as an expected hourly temperature, an expectedhourly pressure, and/or an expected hourly humidity, and based on thisambient condition information, generate an expected hourly electricaldemand. The forecasting component 404 can execute any suitableforecasting algorithms for generating an hourly predicted electricalload based on a given set of hourly ambient data.

The profile generation component 406 combines at least a subset of theforecast data 710 with the model data 416 representing asset performanceand parts-life for the gas turbines to form an optimization problem withan objective of maximizing profit subject to maintaining one or morespecified parts-life targets and other operability conditions. In thepresent example, the variables of the problem are plant output MW andoperating temperature T, which may be an exhaust temperature, an inlettemperature, or another factor indicative of the overall operatingtemperature. The plant output variable MW is associated with the abilityto peak-fire the gas turbines to capture extra revenue, and theoperating temperature T sets the level of CPL operation.

The profile generation component 406 solves the optimization problem toyield an operating profile 706 (also referred to as an operatingschedule), which defines recommended hourly operating parameters—interms of power output MW and operating temperature T in the presentexample—determined to maximize profit for the maintenance intervalwithout significantly changing the target life for the asset (that is,without significantly changing the duration of the maintenanceinterval). The operating profile 706 identifies suitable peak-firingopportunities within the maintenance interval as well as suitableperiods during which to operate in CPL mode in order to compensate forthe additional FFH consumed during the peak-fire periods. In this way,the system 402 generates an operating profile 706 serves as a guide tosubstantially maximizing utilization of the plant asset withoutshortening the maintenance interval or otherwise increasing customerservice agreement charges.

The profile generation component 406 also generates an estimated optimalprice of life value λ* 708, which is derived in parallel with theoperating profile 706, as will be described in more detail below. Thisprice of life value will be used subsequently by the system 402 duringreal-time planning and operation to identify optimal peak-fireopportunities given real-time and historical conditions, to generate andpresent cost-of-life information to the user in meaningful ways intendedto guide optimal asset operation, and for other purposes to be disclosedherein. The price of life value, used as a factor for determining amaximally profitable operating schedule, can also reduce thecomputational burden of solving the maximization problem over longoperating horizons.

FIG. 10 is a computational block diagram illustrating the iterativeanalysis performed on the forecast data 710 and the model data 416 bythe profile generation component 406 to produce the substantiallyoptimized operating profile 706. As will be discussed in more detailbelow, similar iterative analysis can be used for both the long-termplanning as well as real-time planning and operation. In general, theprofile generation component 406 uses a two-layer iteration approach tosolve the optimization problem, whereby the iteration processingcomprises an inner loop 1002 and an outer loop 1004. The problem solvedby iterations of the inner and outer loops can be defined as maximizingthe profit generated over the maintenance interval subject to the targetparts-life constraint. In mathematical terms, this problem can be statedas:

Maximize$\sum\limits_{t = 1}^{T_{M.I.}}\left\{ {{\left( {{electricity}\mspace{14mu} {price}} \right)\left( {M\; W} \right)} - {\left( {{fuel}\mspace{14mu} {price}} \right)(T)}} \right\}$Subject  to${\sum\limits_{t = 1}^{T_{M.I.}}{FFH}} \leq {{target}\mspace{14mu} {life}}$

where T_(M.I.) is the duration of the maintenance interval, and therange of operating temperature T is limited by the operating temperaturerange of the turbines (i.e., T_(min)≤T≤T_(max)). Although the exampleanalysis described herein assumes a parts-life metric defined in termsof FFH, it is to be appreciated that other metrics for parts-life mayalso be used without departing from the scope of one or more embodimentsof this disclosure. Moreover, while the examples described herein assumetime-based operating profiles and forecast data, some embodiments of theprofile generation component 406 can also perform a statistical-basedanalysis that generates a condition-based operating profile rather thana time-based profile.

Performing this optimization for multiple iterations over a largemaintenance interval (e.g., 32,000 hours) can result in a largeoptimization problem. In order to reduce the computational burdenassociated with solving the optimization problem, the profile generationcomponent 406 uses the price of life metric λ to account for creation(by CPL operation) and exhaustion (by peak-fire operation) of FFHcredits by computing the cost of the FFH created or consumed, andfactoring this cost into the profit calculation.

In general, the inner loop 1002 finds an hourly schedule of power outputMW and operating temperature T determined to maximize or substantiallymaximize the profit over the maintenance duration (e.g., by maximizingequation (1)), given an estimated price of life λ. This may includeidentifying an hourly schedule of MW and T that balances peak-firedurations (which consumed FFH credits) with CPL durations (which createsFFH credits) in a manner determined to maximize profit for themaintenance duration as a whole.

Since the profit for a given hour depends on the electricity prices andthe fuel costs for that hour, the profile generation component 406 usesthe predicted market information included in forecast data 710—theelectricity prices and gas prices—together with the asset performancemodels represented by model data 416. For example, the profit given byequation (1) above can be rewritten as

Profit=ElecPrice(t)*MW(t)−FuelPrice(t)*FuelUsed(MW(t),T(t))−λ*FFH_consumed(MW(t),T(t))  (2)

where ElecPrice(t) is the forecasted price of electricity as a functionof time, and FuelPrice(t) is the forecasted price of fuel (e.g., gasprices in the case of gas turbines) as a function of time, both of whichare obtained from the hourly forecast data 710. Note that the profitgiven by equation (2) takes into account the cost of life (FFH)consumed, as represented by a product of the price of life λ and thenumber of FFH consumed as a result of generating a power output MW at anoperating temperature of T for the time unit (e.g., hour) in question.

As noted above, the values of FuelUsed(MW(t), T(t)) andFFH_consumed*(MW(t), T(t)) can be obtained for each iteration of theinner loop based on the model data 416, which includes performance modeldata FuelUsed(MW, T) and parts life model data FFH_consumed(MW, T). Thismodel data can be stored as respective mathematical functions or as anarray of pre-calculated values (as illustrated in FIG. 8). Storing themodel data as pre-calculated values can significantly improve executiontime of the iterations by reducing the inner loop optimization toessentially arithmetic operations followed by comparisons, therebyeliminating the need to evaluate mathematical functions over successiveouter loop iterations.

After the forecast data 710 has been provided to the dispatchoptimization system 402, the user can initiate a long-term operatingprofile generation sequence that generates a long-term recommendedoperating profile for a given maintenance interval (the maintenanceinterval covered by the time range of the forecast data 710). In someembodiments, the user interface component 408 can generate interfacedisplays that allow the user to enter one or more additional constraints(e.g., via user interface component 408) prior to initiation of theprofile generation sequence. These user-defined constraints can include,for example, upper and lower limits on the operating temperature orpower output, modifications to the desired operating horizon (that is,modifications to the target duration of the maintenance interval),identification of days during which the gas turbines are not allowed torun (e.g., based on plant shut-down or planned gas turbine outageschedules schedules), or other such constraints. In addition, in somescenarios the forecasted load or electrical demand for each hour of themaintenance interval can be used as a constraint on the optimizationproblem. For example, in some embodiments the user interface component408 can allow the user to specify that the power output for a given hourof the maintenance interval is not to exceed the forecasted load forthat hour, or is not to exceed the forecasted load plus a specifiedtolerance adder. In addition, additional operating variablescorresponding to other options available to a customer such asduct-burners, evaporative coolers, chillers, etc. can be added to thedispatch optimization system.

Initially, for the first set of iterations of the inner loop 1002, aninitial price of life estimate λ₀ 1006 is used. Using this initial priceof life estimate, the profile generation component 406 determines aninitial hourly schedule of MW and T determined to maximize the profitgiven by equation (2) for each hour of the maintenance interval beingplanned (although the example described herein assumes an hourly timebase, other time units are also within the scope of one or moreembodiments). That is, the profile generation component 406 determines,for each time unit t=0 to T_(M.I.) (where T_(M.I.) is the number of timeunits in the maintenance interval; e.g., 32,000 fired hours), values ofthe power output MW(t) and operating temperature T(t) that maximize orsubstantially maximize equation (2) for each hour. During this innerloop processing, the profile generation component 406 can reference theforecast data 710 to determine the predicted electricity priceElecPrice(t) and fuel price FuelPrice(t) for a given time unit (e.g. anhour) t. The profile generation component can also reference model data416 to determine the expected amount of fuel consumed FuelUsed(MW(t),T(t)) and the expected amount of FFH consumed FFH_consumed(MW(t), T(t))for a given combination of power output MW and operating temperature T.The profile generation component 406 may perform multiple iterations ofthe inner loop for each hour in order to converge on values of MW and Tthat maximum profit for that hour, given the expected electricityprices, expected fuel prices (e.g., gas prices), and estimated price oflife (as well as any defined upper and lower limits on MW and T).

The hourly schedule of MW and T generated as a result of the firstexecution of the inner loop 1002 represents a provisional operatingschedule, subject to verification that the operating schedule satisfiesthe target life constraint. After the inner loop processing hasdetermined this provisional hourly operating schedule of MW and Tdetermined to maximize profit for the maintenance interval (the firstexecution of inner loop 1002), the profile generation component 406executes the outer loop 1004 to determine whether the provisionaloperating schedule satisfies the target life constraint. As noted above,the target life constraint can be described as

${\sum\limits_{t = 1}^{T_{M.I.}}{FFH}} \leq {{target}\mspace{14mu} {life}}$

In general, the profile generation component 406 seeks to determine aprofit-maximizing hourly operating profile for the maintenance intervalthat also keeps the total number of consumed FFH at or below the targetlife of the maintenance interval (e.g., 32,000 fired hours). That is,the amount of extra FFH life consumed during peak-fire hours defined bythe operating profile should be substantially equal (within a definedtolerance) to an amount of extra FFH life credited during CPL hoursdefined by the operating profile. This ensures that the maintenanceinterval will not be shortened and associated additional maintenancecosts will not be incurred, while at the same time optimizingutilization of the plant asset's capacity over the maintenance intervalgiven forecasted conditions.

To this end, once a provisional operating profile has been generated bythe inner loop processing, the profile generation component 406 executesthe outer loop 1004 of the iterative processing, which determines thetotal number of FFH that will be consumed as a result of operating theassets in accordance with the provisional operating profile anddetermines whether the number of consumed FFH satisfies the target lifeconstraint. In general, the sum of the FFH consumed over the maintenanceinterval should be equal to or less than the target life (e.g., 32,000FFH). In some embodiments, the profile generation component 406 candetermine the amount of parts-life consumed over the maintenanceinterval based on the parts-life model data and the scheduled values ofMW and T for the operating profile, such that the constraint can begiven by

${\sum\limits_{t = 1}^{T_{M.I.}}{{FFH\_ consumed}\left( {{M\; {W(t)}},{T(t)}} \right)}} \leq {{target}\mspace{14mu} {life}}$

The outer loop 1004 of the iterative processing performed by the profilegeneration component 406 compares the target life for the gas turbineswith the total amount of FFH that would be consumed over the duration ofthe maintenance interval if the gas turbines were operated in accordancewith the provisional operating profile. If the total consumed FFH isfound to be greater than the target life (outside a defined tolerancewindow), it is assumed that the price of life λ₀ was underestimatedduring the first execution of the inner loop, and the profile generationcomponent 406 increases the estimated price of life λ₁ for the nextexecution of the inner loop. Alternatively, if the total consumed FFH isfound to be less than the target life (outside of a defined tolerancewindow), it is assumed that the estimated price of life λ₀ wasoverestimated during the first execution of the inner loop, and theprofile generation component 406 decreases the estimated price of lifeλ₁ for the next execution of the inner loop. The profile generationcomponent 406 then re-executes inner loop processing using the updatedprice of life value λ₁, and a new provisional operating profile isgenerated, wherein the new provisional operating profile comprises anupdated hourly schedule of power output MW and operating temperature Tcalculated based on equation (2) using the same forecast data 710 andthe updated price of life value λ₁. The profile generation component 406performs multiple iterations of the inner and outer loop processing inthis manner—calculating new provisional operating profiles and adjustingthe price of life value λ_(i) (where i is the iteration index) if thecalculated amount of consumed FFH does not satisfy the target lifeconstraint—until an optimal price of life λ* is found that causes theinner loop to generate an operating profile that satisfies the targetlife constraint.

In general, the profile generation component 406 iterates over multiplevalues of the price of life λ_(i) in the outer loop, solving the innerloop for each for each value of λ_(i), and terminating the outer loopbased on the responses received from the inner loop to past price oflife values. In some embodiments, the manner in which the outer loopselects the next price of life value λ_(i) may depend on the past valuesof λ_(i) and the corresponding inner loop responses. If the inner loopresponse to the current value of j results in violation of the targetlife constraint, the outer loop can decide to increase λ_(i+1) for thenext iteration, and continue to do so until the inner loop responseproduces an operating profile that satisfies the life target. In theother direction, if the inner loop response given a price of life valueλ_(i) does not violate the life target (e.g., the provisional operatingprofile consumes an estimated amount of FFH less than the target life),the outer loop can increase λ_(i+1) for the next iteration to bring theestimated total consumed FFH closer to the target life. The inner andouter loop iterations can then be terminated when the consumed FFH forthe current provisional operating profile is approximately equal to thetarget life (within a defined tolerance).

When the iterations are terminated as a result of the target life beingsatisfied, the user interface component 408 can output the operatingprofile 706 generated by the last iteration of the inner loop as therecommended operating profile for the gas turbines. The system 402 alsooutputs the presumed optimal price of life value λ* that gave rise tothis operating profile. In some embodiments, the resulting operatingprofile can be rendered as a recommended hourly schedule of gas turbinepower output and operating temperature for each day of the maintenanceinterval. FIG. 11 is an example display format for an operating profile.As shown in this example, each day is divided into hours (Hour Ending 1,Hour Ending 2, etc.), with each hour specifying a recommended plantoutput and operating temperature (exhaust temperature in this example)defined by the operating profile 706.

In some embodiments, the user interface component 408 can output one ormore graphical representations of the optimized operating profile 606,as well as any other factors on which the profile was based. FIG. 12 isan example graphic that plots the recommended operating temperaturedefined by the operating profile over the duration of the maintenanceinterval (graph 1204) together with the predicted hourly electricityprices over the same maintenance interval (graph 1202). User interfacecomponent 408 can allow the user to add or remove variables from thegraphical display (e.g., fuel prices, expected electrical demand) forcomparison with the recommended operating profile.

FIG. 13 is another example graphic display 1302 that can be generated byuser interface component 408 based on results of the long-term analysisdescribed above. Graphic display 1302 is an example long-term planningdisplay of the dispatch optimization system 402, and presents a dailyview of the long-term operating profile. A power bar graph 1312represents the daily power output for a selected month (November 2016),as determined based on the recommended hourly power outputs defined bythe operating profile 706. Graph 1312 is plotted together with anambient temperature bar graph 1314, a fuel cost bar graph 1316, and apower price bar graph 1318, representing the average daily temperatures,fuel costs, and electricity prices, respectively. The information in theambient temperature graph 1314, fuel cost graph 1316, and power pricegraph 1318 can be obtained from the forecast data 710. A monthlyselector graphic 1306 allows the user to select a month to be viewedand/or optimized. Outage date entry fields 1304 allows the user to enterdays during which individual gas turbans will be out of service, whichcan be taken into consideration by the dispatch optimization system 402when running the optimization routine (e.g., by limiting the possiblepower output on a given day based on the number of turbines expected tobe available). A shut-down entry area 1310 allows the user to enterstart and end dates for planned plant shut-downs, which is also takeninto consideration by the optimization system 402. In the illustratedexample, the shut-down is defined to begin on November 1 and to end onNovember 10, which is reflected in the power output graph 1312. When aplant shut-down is defined the profile generation component 406 assumesthat no power will be output or parts-life consumed on those days, andthe inner and outer loops of the iterative process are executed usingthese definitions as constraints. A “Run Optimizer” graphical button1308 initiates (or re-initiates) the iterative process that generates along-term operating profile for a maintenance interval (or otheroperating horizon) based on the available forecast data, plantefficiency and parts-life models, and any user-defined operatingconstraints (e.g., plant shut-down or asset outage dates).

FIG. 14 is another example graphical display 1402 that can be generatedby user interface component 408 according to one or more embodiments.Graph 1406 represents the hourly operating temperature defined by theoperating profile 706, depicted in terms of the deviation of theoperating temperature from baseline operation (referred to as the deltafiring temperature). Temperatures that fall below a certain deltatemperature represent CPL operation, during which FFH (or other units ofparts-life) are banked, while temperatures above the baseline (the zeroaxis of graph 1406) represent peak-fire operation during which FFH areconsumed. Graph 1404, plotted over the same timeline as graph 1406,illustrates the expected incremental profit generated over themaintenance interval as a result of operating the plant assets inaccordance with the operating profile 706. The incremental profit can becalculated by the parts-life metric component 412 as an hourlycumulative value based on equation (2) above (or another suitable profitequation that includes the price of life as a factor) using theforecasted fuel and electricity price data and the recommended values ofMW and T defined by the operating profile 706 for each hour.

In some embodiments, the cold part load/peak-fire optimization curveplotted in graph 1406 can be color-coded to more clearly convey the CPLand peak-fire periods. For example, portions of the curve that fallbelow the delta temperature representing CPL operation can be coloredblue, while portions of the curve that exceed the zerodelta—representing peak-fire operation—can be colored red.

It is to be appreciated that the manner in which the long-term operatingprofile for the maintenance interval is presented by the user interfacecomponent 408 is not limited to the examples illustrated herein. Rather,information relating to the hourly or daily recommended operatingschedule, the forecasted ambient and market information, parts-lifesavings and utilization, and profit calculations can be presented in anysuitable format, and in any combination, without departing from thescope of one or more embodiments of this disclosure.

In some embodiments, in addition to generating graphical reports andguides based on the long-term operating profile 706, the controlinterface component 414 of the dispatch optimization system 402 canexport the long-term operating profile to a plant asset control orscheduling system, so that that the operating schedule represented bythe profile is automatically programmed into the asset's control system.

The nominal long-term operating profile 706 generated by the profilegeneration component 406 is based on predicted ambient and marketconditions represented by the forecast data 710. However, duringsubsequent real-time operation of the power-generating plant assetsduring the maintenance interval, it is likely that the actual ambientconditions, fuel costs, and electricity prices will deviate from theircorresponding predicted values represented by forecast data 710.Accordingly, while the long-term operating profile 706 can bebeneficially used for long-term planning prior to the start of themaintenance interval being planned, the dispatch optimization system 402can update the operating profile during the maintenance interval basedon updated, real-time ambient and market information, as well as actualasset operation information (both real-time and historical). As will bedescribed in more detail below, updated iterations of the inner andouter loop can be performed in connection with both day-ahead andreal-time (i.e., same day, or hourly) planning and operations.

FIG. 15 is a block diagram illustrating example data inputs and outputsfor the profile generation component 406 of the dispatch optimizationsystem 402 during the day-ahead and real-time planning and execution.During operation of the plant assets within the maintenance interval,the dispatch optimization system 402 can be provided with updatedforecast data 710 for at least a portion of the remainder of themaintenance interval (e.g., t=t_(k) to t_(M.I.), where t_(k) is thepresent time and t_(M.I.) is the end of the maintenance interval oroperating horizon). The dispatch optimization system 402 can also beprovided with real-time and historical data 1502 for the present timeand at least a subset of the previous duration of the maintenanceinterval (e.g., t=1 to t_(k)). This real-time and historical data 1502can include, for example, updated electricity and fuel price dataobtained from appropriate market websites or other sources of marketdata, updated ambient data obtained from weather forecasting websites,historical operating data representing actual past operation of theplant assets (e.g., historical hourly power output and operatingtemperatures for previous hours of the maintenance interval) obtainedfrom the asset control system, or other such data. Some or all of thereal-time and historical data 1502 can be obtained from the respectivedata sources by real-time data acquisition component 410. The profilegeneration component 406 can substitute this updated real-time andhistorical data 1502 for previously forecasted information, andre-execute the iterative analysis described above in connection withFIG. 10 to yield an updated operating profile 1506 to facilitateday-ahead planning or real-time planning and execution. Re-executing theprofile generation sequence using actual historical operating data forpast hours of the maintenance interval allows the operating plan to beupdated to reflect actual historical operation of the plant assets,which may deviate from the predicted operation defined by the previouslygenerated long-term operating profile.

The profile generation component 406 will also generate an updated priceof life value 1508 based on the real-time and historical data 1502 (aswell as the updated forecast data 710), which can be used in a number ofways to present operation planning recommendations to the user inmeaningful ways. As will be described below, the updated price of lifevalue also serves as a real-time feedback mechanism to ensure that assetoperation will converge on the target life constraint even if pastoperation has deviated from forecasted performance (e.g., by excessivepeak-firing or CPL operation), while also substantially maximizingprofit for the remainder of the maintenance interval. In someembodiments, during day-ahead or real-time planning, the profilegeneration component 406 can selectively execute either the inner looponly in order to generate an updated operating profile 1506 using theupdated forecast and real-time data together with the current value ofthe optimal price of life λ*, or both the inner and outer loop in orderto generate both an updated operating profile 1506 and an updatedoptimal price of life value λ*.

In an example implementation, during operation within the maintenanceinterval, the profile generation component 406 may run both the innerand outer loop iterations only once per day based on the new forecastdata 710 and real-time and historical data 1502 in order to yield anupdated optimal price of life value λ* 1508 and an updated operatingprofile 1506. The new price of life λ* and updated operating profile canbe used to generate a nominal day-ahead schedule for the next operatingday. When the new day has begun, the profile generation component 406can execute, on a more frequent basis (e.g., every hour or every fewminutes), the inner loop only—using the updated optimal price of life,the updated forecast data 710, and the real-time conditions—in order togenerate updated operating recommendations (e.g., CPL and peak-firerecommendation data).

Execution of the inner and outer loop iterations during the maintenanceinterval for day-ahead and real-time planning is similar to theiterative sequence performed during the long-term planning stage (priorto the start of the maintenance interval), with previously providedforecast data replaced with real-time and actual historical operationdata as appropriate. When the loops are executed during the maintenanceinterval for day-ahead or real-time planning, the profile generationcomponent 406 can use current ambient and market data as well as updatedforecast data 710 to solve the inner loop maximization for the currenthour and each remaining hour of the maintenance interval (e.g., fortimes t=t_(k) to t_(M.I.)). The profile generation component 406 can usethe current value of the optimal price of life λ* (that is, the optimalprice of life value obtained during the most recent previous inner andouter loop execution) as the initial price of life value 1006 (see FIG.10). For the portion of the maintenance interval that has already passed(times t=1 to t_(k)), the profile generation component 406 can calculatethe actual amount of parts-life that has already been consumed(including parts-life that has been banked as a result of CPL operationthus far during the maintenance interval) based on the actual historicaloperating data (that is, the actual hourly values of MW and T for timest=1 to t_(k)).

If only the inner loop is to be executed, the updated operating profile1506 produced as a result of maximizing the profit for the remaininghours of the maintenance interval is taken to be the new operatingprofile, and the iteration is terminated. When only the inner loop isbeing re-executed, the previously obtained optimal price of life valueλ* is assumed to still be accurate, and the updated operating profile1506 is generated based on this previously obtained optimal price oflife λ*.

If the outer loop also to be executed (typically less frequently thanexecution of the inner loop alone; e.g., once a day), the profilegeneration component 406 can determine the sum of the actual amount ofparts-life consumed during the past portion of the maintenance intervaland the predicted amount of parts-life that will be consumed over theremainder of the maintenance interval as a result of executing theassets in accordance with the updated operating profile. As noted above,the profile generation component 406 can calculate the actual amount ofparts-life that has already been consumed based on the actual historicaloperating data (that is, the actual hourly values of MW and T for timest=1 to t_(k)) and the parts-life model data for the assets. Once the sumof actual consumed parts-life and predicted parts-life consumption hasbeen determined, the profile generation component 406 can determine, asthe outer loop analyses, whether this sum satisfies the parts-lifeconstraint, adjust the optimal parts life λ* as needed, and re-executethe inner loop. Similar to the long-term planning analysis, the innerand outer loops are iterated in this manner until the sum of the actualand predicted consumed parts-life satisfies the target parts-lifeconstraint. In general, the outer loop constraint for generation of theupdated operating profile 1506 and updated price of life 1508 during themaintenance interval for day-ahead or real-time planning can be given by

${{\sum\limits_{t = 1}^{t_{k}}{FFH}_{{consumed}{({{M\; {W_{act}{(t)}}},{T_{act}{(t)}}})}}} + {\sum\limits_{t = t_{k}}^{t_{M.I.}}{FFH}_{{consumed}{({{M\; {W{(t)}}},{T{(t)}}})}}}} \leq {{target}\mspace{14mu} {life}}$

where MW_(act)(t) and T_(act)(t) are the actual values of the poweroutput and operating temperature, respectively, based on historicaloperation of the plant assets during the maintenance interval up to thepresent time (t=1 to t_(k)), and MW(t) and T(t) are the future values ofMW and T defined in the updated operating profile 1506 for each hour(t=t_(k) to t_(M.I.)) remaining in the maintenance interval.FFH_(consumed)(⋅) are the plant/asset parts-life models included inmodel data 416. Reiterating the inner and outer loop processing on adaily basis (or on another time basis) during operation within themaintenance interval can refine the recommended operating profile basedon real-time conditions and actual historical operational data withoutsignificantly changing the operating horizon of the assets.

The dispatch optimization system 402 can use the information generatedby the profile generation component—including the updated operatingprofile 1506 and the estimated optimal price of life λ*—to presentmeaningful information to plant operators and managers intended to guideoptimal and profitable operation of the plant assets. This can include,for example, reporting the value of parts-life saved under givenoperating conditions in terms of the amount of MWh of peak-firing thesaved parts-life is worth, reporting the estimated number of hours ofpeak-fire that can be performed before running out of banked parts-lifecredited as a result of previous CPL operation (e.g., banked FFH),reporting the quantity of parts-life that would be saved by operating inCPL mode at the present time in terms of the number of MWh of peak-firethat will be credited, the favorability of operating in CPL mode at thepresent time in terms of incremental fuel burned versus the creditedMWh, or other such metrics. These metrics can be generated by theparts-life metric component 412 based on available data (currentoperating profile, estimated optimal price of life, current andforecasted ambient and market data, historical operating data, etc.).

FIG. 16 is an example display screen 1602 that presents day-aheadcapacity information generated by the dispatch optimization system 402.Information presented on day-ahead planning displays can serve as guidesfor plant managers or next-day traders in connection with determiningwhether there is a cost benefit to peak-firing plant assets the nextday, and determining a minimum price at which the additional outputshould be sold. Day-ahead capacity display screen 1602 (or a similardisplay screen) can be invoked via an overview display screen such asdisplay 602 illustrated in FIG. 6. In this example, display screen 1602,which can be generated by user interface component 408, displaysday-ahead planning information for a 3×1 combined cycle plant,comprising three gas turbines (Gas Turbines 1, 2, and 3), a heatrecovery steam generator, and a steam turbine. Display screen 1602overlays capacity information on a graphic representing the 3×1 system.For each gas turbine, the user interface component 408 displays thenext-day peak capacity 1604, baseline capacity 1606, and degradationfactor 1608. Display screen 1602 also displays, for each gas turbine,the recommended minimum peak electricity price 1610 at which to sellpeak output of the gas turbine for the next day. The parts-life metriccomponent 412 can calculate the recommended minimum peak electricityprice based on the long-term data defined in the operating profile, theoptimal price of life value λ*, and the forecasted market data.

FIG. 17 is an example display screen 1702 that allows a user to enterday-ahead bid and operating information for the next operating day.Example display screen 1702 includes a plant output bar chart 1704 thatgraphically illustrates the planned hourly output for the plant for thenext day. In some scenarios, the planned output may be derived from therecommended hourly MW defined in the updated operating profile 1506.Alternatively, the planned output values may be entered by the userbased on known conditions (e.g., expected available gas turbinecapacities, maintenance schedules, expected demand, etc.). Displayscreen 1702 also includes an electricity price bar chart 1706 indicatingthe cleared hourly electricity prices for the next day. These prices maybe obtained by the system 402 from forecast data 710, or may be enteredby the user as minimum prices that have been approved for each hour.

In this example, a file selection area 1708 allows a user to enter oneor more of updated ambient condition data, updated electricity pricedata, gas turbine status data (e.g., available or unavailable), orupdated power output limits for the next day (or beyond). Although theillustrated example depicts the ability to manually provide this updatedinformation, in some embodiments of the real-time data acquisitioncomponent 410 can obtain at least some of this information automaticallyfrom relevant sources (e.g., weather forecast websites, electricitymarket websites, maintenance databases, etc.). Display screen 1702 alsoincludes a field 1710 that allows the user to enter an updated expectedfuel price (e.g., in $/MBTU) for the day ahead. A Submit button 1712allows the user to initiate another execution of the optimizationsequence based on the updated information provided by the user. In someembodiments, both the inner and outer loop will be re-executed inresponse to initiation of the optimization sequence for day-aheadplanning, resulting in both an updated operating profile and an updatedprice of life value λ*. When the optimization sequence is complete, thesystem 402 can update the values depicted in charts 1704 and 1706 toreflect the new nominal output schedule for the next day. The system 402can also allow the user to modify one or more of the MW or electricityprice values if desired through interaction with the display screen1702. Upon approval of the nominal output schedule and pricing,selection of button 1714 causes the system to proceed to the day-aheadplanning display screen using the nominal values.

FIG. 18 is an example day-ahead planning display screen 1802. Displayscreen 1802 includes an hourly output profile bar chart 1804representing the hourly MW output defined by the updated operatingprofile 1506 (including any modifications to the profile or associatedconstraints entered by the user via display screen 1702). The hourlyoutput data is charted together with lines 1808 and 1810 representingthe peak load MW projection and baseload MW projection, respectively.The bars for each hour can be color-coded to readily identify hours thatare good candidates for saving parts-life by running the plant assets inCPL operation (also referred to as variable temperature control, orVTC). For example, hours with light-shaded bars (e.g., bar 1812)represent hours during which the assets will be running at part-load,and are therefore candidates for CPL operation. Hours with medium-shadedbars (e.g., bar 1814) represent hours during which the assets areexpected to be running at baseload, and therefore are not candidates forCPL operation. Hours with dark-shaded bars (e.g., bar 1816) representhours during which the assets may beneficially be peak fired to runabove base load—thereby generating one or more extra MWh forsale—provided the additional parts-life cost associated with peak-firinghas been compensated for by CPL operation at another time during themaintenance interval.

Display screen 1802 also includes an hourly fuel cost bar chart 1806,which is populated with fuel cost data (in $/MBtu) obtained from theforecast data 710. In the illustrated example, in order to compensatefor the additional parts-life consumed by peak-fire operation, forexample at Hour Ending 15, the assets can be operated in CPL mode duringone or more other hours during the day that are indicated as beingcandidates for CPL operation (VTC). Since CPL operation is lessfuel-efficient, the amount of parts-life saved, equivalently peak-fireMWh credited, by CPL operation during a given hour has an associatedcost (e.g., fuel price [$/MBtu]×extra CPL fuel [MBtu]) that is at leastpartly a function of the fuel cost for that hour. In the illustratedexample, the favorability of operating in CPL for the differentcandidate hours during the day is displayed in plot 1806 as a ratio ofthe aforementioned CPL fuel costs for those hours and the respectivepeak-fire MWh credits—a quantity in $/MWh. Accordingly, based oninformation generated by the optimization sequence, the dispatchoptimization system 402 can automatically select which of theCPL-eligible hours are optimal times during which to operate in CPL modein order to generate banked parts-life (e.g., FFH) that can be consumedduring the peak-fire hour. Since the optimization system 402 computes amaximum $/MWh at which it is worth saving parts-life, the system canselect one or more hours during which the fuel costs are at or belowthis parts-life cost, and flag these hours for CPL operation.Alternatively, in some embodiments, the decision of which hours tooperate in CPL mode and the extent of CPL operation at such hours may beleft to the user to decide on the basis of the various guiding metrics.

A MWh deposit area 1818 of display screen 1802 conveys a total amount ofMWh savings accumulated for the 24-hour day-ahead period. In thisexample, bar 1820 represents the total amount of MWh projected to bebanked as a result of CPL operation over the ensuing 24-hour period.This value represents the number of banked (or credited) peak-fire MWhthat can be generated during the maintenance interval without violatingthe target life constraint as a result of the parts-life saved from CPLoperation during the 24-hour period.

Bar 1822 represents the average fuel cost per saved MWh over the 24-hourperiod. Bar 1824 represents a recommended electricity price ($/MWh) atwhich to peak-fire the plant assets to deploy the banked MWh. Thisrecommended price is based on the fuel costs incurred as a result ofbanking the additional MWh through CPL operation. The difference betweenthe recommended peak price 1824 and the average fuel cost 1822represents the incremental profit generated as a result of deploying thebanked MWh at the indicated peak electricity price. In some embodiments,the system 402 can identify suitable peak-fire hours during which theassets can profitably generate MWh based, at least in part, on adetermination of which hours are associated with forecasted electricityprices that are equal to or greater than the recommended minimum peakprice represented by bar 1824. In such embodiments, the user interfacecomponent 408 may identify these recommended peak-fire hours on theday-ahead hourly profile chart 1804 by color-coding the bars associatedwith those hours.

In various embodiments, the dispatch optimization system 402 can beconfigured to perform either automatic or advisory actions in responseto CPL operation or peak-fire decisions. For example, in some scenarios,once suitable hours of CPL operation have been identified, the controlinterface component 414 of the dispatch optimization system 402 canautomatically instruct the plant asset control system to operate in CPLmode during the selected hours. Alternatively, the system 402 may onlyindicate the recommended hours of CPL operation to the user via displayscreen 1802 (or another display screen), allowing the user to manuallyset the recommended CPL operation according to their own discretion.Similarly, when the optimization system 402 has identified suitablehours during which to peak-fire the assets in order to consume bankedMWh, the system 402 can either automatically configure the asset controlsystem to carry out the recommended peak-fire operations or convey therecommendation to the user. The user may also configure the dispatchoptimization system 402 to enact a mix of automatic and advisorycontrol. For example, a user may decide to configure the system 402 suchthat CPL operation decisions are automatically implemented by thecontrol interface component 414, while peak-fire operation decisions areadvisory-only, requiring the user to manually set peak-fire operation.

The day-ahead plan depicted on display screen 1802 represents a nominaloperating plant for the next day. Plant personnel can use this day-aheadinformation as a nominal starting point for the day's operations, whichwill be refined in real-time based on actual real-time conditions andthe most recently calculated price of life value λ*, which representsthe sensitivity between parts-life and profit.

The optimization system 402 can generate real-time planning andexecution screens that allow the user to monitor the plant assets duringexecution of the plan and, if desired, modify operation relative to theday-ahead plan. FIG. 19 is an example real-time execution display screen1902 that can be generated by the user interface component 408 of thedispatch optimization system 402. Display screen 1902 is intended foruse during operation of the plant assets for the current day, and may besuitable for use by plant operators.

In this example, display screen 1902 includes a VTC status area 1906identifying the part load and VTC statuses for the three gas turbines(GT 1, GT 2, and GT 3). The first column of VTC status area 1906indicates the dispatch optimization enable statuses for the gasturbines. When the indicator in this column is green, the CPL operationfor the corresponding gas turbine is running under the control of theoptimization system 402, such that the optimization system 402 controlswhich hours the plant assets are operated in CPL mode based on currentreal-time conditions and the presumed optimal price of live value λ*(calculated by the most recent execution of the inner and outer loopiterations by the profile generation component 406). In someembodiments, operators can manually enable and disable optimizer controlof CPL operation as desired, either through interaction with anappropriate display screen of the optimization system 402 or through anexternal control of the plant asset control system.

The last column of the VTC status area 1906 displays the current deltatemperature relative to nominal operating temperature (e.g., the hotload path of FIG. 2) for the three gas turbines, respectively. Asindicated in the fields of this column, gas turbine 1 is currentlyrunning at nominal operating temperature with a delta temperature ofzero, gas turbine 2 is currently running 74.4° F. below the nominaloperating temperature, and gas turbine 3 is currently running 38.2° F.below the nominal operating temperature. An appropriate deltatemperature (also referred to as the VTC shift) for each turbine can becalculated by the parts-life metric component 412 based on the optimizedoperating profile 1506. The VTC shift (that is, the extent of CPLoperation as given by the delta temperature) is a function of thecurrent load on the respective gas turbines and the current operatingtemperature (e.g., exhaust temperature in the present example) of therespective gas turbines, as well as the electricity and fuel prices forthe current hour obtained from the day-ahead data previously obtained bythe system 402, together with the price of life value λ*. For example,for gas turbine 2, the optimization system 402 has determined that CPLoperation at a delta temperature of −74.4° F. yields, for the presenthour, an optimal trade-off between parts-life saved and profit given thecurrent fuel and electricity prices as well as the determined price oflife λ* (which is an estimated metric representing optimal trade-offbetween parts-life and profit).

For closed-loop control of the plant assets during real-time operation,the dispatch optimization system 402 can determine the optimal degree ofCPL operation based on the current price of life value and currentambient and operating conditions, and automatically set the deltatemperature in accordance with this calculated optimal degree of CPLoperation. In an example approach, the parts-life metrics component 412can use the optimal price of life value λ* determined during theday-ahead planning phase to calculate the extent of CPL operation basedon current conditions by determining the operating temperature T thatmaximizes the profit given by equation (2) (or a similar profitrelationship), where all other variables used in the equation (e.g., theMW output) are current values. In another approach, the outputs of theplanning phase can be viewed as an allocation of parts-life savings todifferent conditions or durations (e.g., daily, hourly, etc.). Theoperating temperature T can then be suppressed at any given instant tothe extent that the present parts-life saving is close to (or consistentwith) that allocated for the appropriate allocation “bucket” determinedby the present condition or instant.

The second column of VTC status area 1906 represents the current partload status for the respective gas turbines. Since gas turbine 1 iscurrently running with a zero delta temperature, the part load statusindicator for gas turbine 1 conveys that the gas turbine is notcurrently running in cold part-load mode. Gas turbines 2 and 3 arecurrently running in CPL mode (as given by their respective negativedelta temperatures).

The third column of VTC status area 1906 represents the VTC status forthe respective gas turbines. This indicator conveys whether therespective gas turbines are currently having their CPL operationcontrolled in accordance with VTC.

The real-time variable temperature control (VTC) shift that is appliedto each of the gas turbines is also conveyed graphically on chart 1904of displays screen 1902. Vertical line 1908 represents the current hourof operation, and the three horizontal lines 1910 represent the deltatemperatures relative to baseline temperature for the three gasturbines, respectively (which correspond to the values given in the lastcolumn of VTC status area 1906).

During real-time (same day) operations, the price of life value λ* maybe assumed to be the same throughout the day. Accordingly, as notedabove, the optimization system 402 may only re-execute both the innerloop and outer loop iterations once per day in order to obtain anupdated daily estimate of the price of life λ* 1508 given the actualhistorical operation and forecasted conditions for the remainder of themaintenance interval. This allows the price of life λ* to be adjusted toreflect any deviations in the expected daily parts-life creation orconsumption relative to the long-term prediction (based on forecastedinformation) after operation of the plant assets within the maintenanceinterval has begun. For example, after the fifth day of operation withinthe maintenance interval, the dispatch optimization system 402 canre-execute the inner and outer loop iterations using the actualhistorical operating data (e.g., hourly actual MW and T values) for thefirst five days of operation, and forecasted ambient and market data forthe remaining hours of the maintenance interval (as described above inconnection with FIG. 15). This re-execution yields a new price of lifevalue λ*, which may be different than the original price of life valuecalculated during the long-term planning stage if the actual amount ofparts-life created or consumed during the first five days of operationdeviates from the predicted amount of parts-life for those days.

Once the new day has begun, the dispatch optimization system 402 can, onan hourly basis or at any desired frequency, re-execute the inner looponly on an hourly basis (or at any desired frequency; e.g., at afrequency that matches the time base of the maintenance interval) usingthe updated price of life value λ*, as well as current real-timeelectricity prices, load, and ambient conditions. The results of theseinner loop executions (e.g., the hourly values of MW and T defined bythe resulting operating profile) can be used as a basis for hourly CPLoperating decisions. In this way, the price of life value λ* serves as areal-time feedback mechanism for keeping plant asset operation on trackfor satisfying the target parts-life constraint, even if the plantassets have been operated during the interval in a manner that deviatesfrom the long-term prediction. For example, if the plant assets arepeak-fired over the course of the first few days of the maintenanceinterval in excess of the recommended peak-fire periods defined by theoriginal long-term operating profile, the daily outer loop executionwill cause the price of life value λ* to increase each day, so thatsubsequent executions of the inner loop will yield CPL and peak-firerecommendations that compensate for this unexpected excessivepeak-firing and bring operation in line to satisfy the target life.

FIG. 20 is an example real-time monitoring display screen 2002 that canbe generated by the user interface component 408 of the dispatchoptimization system 402. Display screen 2002 displays current andhistorical cumulative information for the three gas turbines for themost recent 90-minute period.

The left side of the display screen 2002 includes a peak bank balancearea 2010 that displays the current or instantaneous cumulative balanceof banked MWh for the three gas turbines. The banked MWh value for eachgas turbine can be calculated by the parts-life metric component 412based on the net amount of parts-life or consumed as a result of CPL andpeak-fire operations. For example, in the example depicted in FIG. 20,gas turbine has −16.7 MWh of peak-fire power available, since gasturbine 1 has peak-fired excessively during the maintenance intervalwithout completely compensating for the consumed parts-life by CPLoperation. The amount of parts-life deficit resulting from thisoperation is translated to a MWh value (−16.7 MWh), which represents theamount of MWh that must be compensated for by CPL operation before theend of the maintenance interval in order to ensure that the target lifefor the asset is satisfied. By contrast, gas turbine 2 has banked moreparts-life as a result of CPL operation than it has consumed bypeak-firing, resulting in a positive peak MWh bank of 11.8 MWh. Thisvalue represents a translation of the banked parts-life into MWhavailable for peak firing.

Display screen 2002 also includes a peak bank balance graph 2004 to theright of peak bank balance area 2010. Peak bank balance graph 2004 plotsthe historical balance of banked MWh over time for each of the three gasturbines for the most recent 90-minute period. A vertical slider bar2016 can be selected and dragged across the graph 2004, and an overlaidwindow 2018 can render the numerical values associated with a point intime corresponding to the slider bar 2016.

Display screen 2002 also includes a cumulative peak-fire MWh area 2012that displays cumulative net amount of additional MWh generated bypeak-fire operation. Ideally, the cumulative peak-fire MWh will begenerated through the consumption of banked MWh that were banked by CPLoperation, or will be compensated for with banked MWh created throughfuture CPL operation within the maintenance interval. In the illustratedexample, gas turbine 1 has generated 17.0 MWh of additional power outputthrough peak-firing, while gas turbines 2 and 3 have not yet used any oftheir banked MWh (11.8 and 10.4, respectively) for peak-firing duringthe current maintenance interval. A cumulative peak-fire graph 2006—tothe right of area 2012—plots the cumulative number of extra MWhgenerated over time for the three gas turbines for the most recent90-minute period. In general, the price at which the additionalpeak-fired MWh were sold represents the gross revenue resulting frompeak-firing of banked MWh (the positive portion of profit).

To obtain the banked MWh values rendered on displays screen 2002, theoptimization system 402 (e.g., the parts-life metric component 412) canfirst compute the number of extra FFH created through CPL operation andthe number of FFH consumed through peak-fire operation from the start ofthe current maintenance interval through the present time. These FFHvalue can be derived, for example, based on the parts-life model datafor the assets together with the hourly historical operation data forthe assets (e.g., the actual plant output MW and operating temperature Tfor each hour of the maintenance interval thus far). The optimizationsystem 402 can then determine the net FFH credit or deficit based on thedifference between the created and consumed FFH values, and convert thisnet number of FFH to a corresponding MWh value. This conversion can bedetermined based on the peak-fire capacity of the respective assets andthe amount of peak-fire MWh that a given gas turbine can produce foreach FFH, which are determined from the performance and parts-lifemodels of the asset, respectively. This conversion technique is onlyintended to be exemplary, and any suitable calculation for converting aparts-life metric (e.g., FFH) to a corresponding peak-fire MWh value iswithin the scope of one or more embodiments of this disclosure.

A cumulative MBTU area 2014 of display screen 2002 displays theinstantaneous accumulated extra fuel (in thousands of British thermalunits, or MBTUs) that has been consumed for each gas turbine as a resultof CPL operation in order to create banked MWh for peak-firing. In theillustrated example, gas turbine 1 has not yet operating in CPL mode,and therefore has not consumed additional fuel as a result of CPLoperation (hence, the negative peak bank balance for gas turbine 1 as aresult of the 17.0 MWh of peak-fire output). Gas turbines 2 and 3 haveconsumed 19.4 and 11.0 MBTU of additional fuel as a result of CPLoperation thus far during the current maintenance interval (resulting inthe 11.8 and 10.4 MWh, respectively, of banked peak MWh indicated inarea 2010, which have not yet been consumed through peak-firing).Cumulative MBTU graph 2008 to the right of area 2014 plots thecumulative amount of extra fuel consumed through CPL operation for thethree gas turbines over the most recent 90-minute period. As indicatedby graph 2008, gas turbines 2 and 3 are currently operating in CPL mode,resulting in both an increasing amount of fuel consumed (graph 2008) andan increasing number of banked MWh (graph 2004) for those two assets. Ingeneral, the price of the fuel consumed represents the costs associatedwith saving the MWh that are deployed during peak-firing (the negativeportion of profit).

The cumulative amount of extra fuel consumed as a result of past CPLoperation can be computed by the system 402 (e.g., the parts-life metriccomponent 412) based on the parts-life model data together with thehistorical hourly operational data (e.g., the actual power output MW andoperating temperature T for each previous hour of the maintenanceinterval). Alternatively, the amount of fuel consumed rendered ondisplay screen 2002 can be based on actual measured fuel consumption bythe assets.

The information rendered on monitoring display screen 2002 translatesthe parts-life metrics tracked by the dispatch optimization system 402into terms that a plant manager or operator is likely to find useful;namely, indications of MWh that are available for peak-firing (or,conversely, indications of MWh that must be compensated for as a resultof excessive peak-firing), and a quantity of fuel consumed in order tocreate banked MWh for peak-firing. Thus, the information on displayscreen 2002 can quickly convey to an operator how many banked MWh areavailable (as a result of previous CPL operation), and when anopportunity to profitably peak-fire the assets arises (e.g., whenelectricity prices are high enough to justify peak-firing, as can beascertained from the peak price indicator 1824 on the day-ahead planningscreen).

It is to be appreciated that the metrics illustrated in the exampledisplay screens described above are not intended to be limiting, andthat other suitable metrics and display formats can be generated by thedispatch optimization system 402 in various embodiments. For example,for some power-generating systems the same amount of parts-life savedunder different operating or ambient conditions can have differentassociated costs (e.g., different amounts of fuel consumption, etc.) dueto nonlinearities, dependence on ambient conditions, or other factors.To account for this variation in life-saving costs, the computedadditional costs of CPL operation under current ambient conditions canbe divided by the associated amount of parts-life saved in terms of peakMWhs to produce a $/MWh value. Smaller values of this $/MWh value meanthat additional MWh can be banked less expensively; accordingly, thisvalue can be used as a guide for identifying suitable CPL opportunities.The heat-rate marginal costs at peak-fire can also be added to this$/MWh value to yield the net marginal cost of peak operation. As withother example metrics illustrated in FIG. 20, this $/MWh CPL cost andthe net marginal cost of peak-firing can be tracked and accumulated overtime.

In another example, the system 402 can guide selection of suitablepeak-fire periods by generating an incremental cost as a function ofpeak MWh (or fired hours at peak capacity), and plotting thisincremental cost on the same horizontal axis with a sorted list ofelectricity price, such that the highest electricity prices coincidewith the lowest incremental CPL costs (i.e., the second curve is abovethe first). In such embodiments, the system can allow the user to entera threshold value (e.g., depending on the risk appetite) as a minimumseparation between the two curves that indirectly specifies the amountof peak MWh the user wishes to bank during the maintenance interval aswell as a minimum price at which these MWh ought to be dispatched. Ifreal-time electricity price data is provided to the dispatchoptimization system 402 (e.g., from an electricity market website oranother source), the system 402 can automatically initiate control ofpeak-fire operation in response to determining that the currentelectricity price is equal to or greater than this minimum price.

In a manual control (i.e., advisory only) example of peak-fire guidance,the system may render a current per-unit cost of the peak MWh bank(and/or its projection over a time horizon) that can help the user todecide whether to peak-fire their assets. If electricity price forecastsare available, then the smallest electricity price at which peak-firingis advisable can be recommended based on expected total MWh credits thatare banked during the operating horizon.

In the examples described above, the target life used as a constraintfor the iterative analysis is assumed to be a constant value. However,in some embodiments the profile generation component 406 can beconfigured to solve the maximization problem using the target lifeitself as another variable (in addition to the power output MW and theoperating temperature T). In such embodiments, the user interfacecomponent 408 can allow the user to define a maximum amount ofacceptable deviation from the baseline target life (which may be basedon a customer service agreement with the equipment manufacturer or othermaintenance provider). This user-defined deviation represents a maximumchange in the target life that the user considers acceptable.Alternatively, the user may define upper and lower limits on the targetlife, instructing the dispatch optimization system 402 that the targetlife for the recommended profile is to remain between these upper andlower boundaries. When the profile generation component 406 performs theiterative analysis described above, rather than terminating theiterative analysis when the estimated FHH is approximately equal to thefixed target life, iterations of the inner and outer loop processingcontinue until a profit-maximizing operating profile is found having atarget life that is within the target life parameters defined by theuser. In some embodiments, the profile generation component 406 maygenerate multiple operating profiles having respective different targetlives that are within the acceptable target life range defined by theuser, and select the profile that yields the highest profit from amongthe multiple profiles, thereby allowing the target life to act as avariable together with the power output and operating temperature.

In some embodiments, the computational burden associated with performingmultiple iterations of the inner loop processing can be further reducedby parallelizing the computational operations. For example, for amaintenance interval comprising 32,000 hours, the profile generationcomponent 406 may divide the maintenance interval into substantiallyequal sections (e.g., 32 1000-hour sections, or other suitablesegmentations of the maintenance interval) and perform the hourly profitmaximization processing for the sections substantially simultaneouslyusing parallel processing. The profit maximization processing can beparallelized in this manner since the inner loop comprises disjointoptimization problems (that is, the solution to the maximization problemfor a given hour of the maintenance interval does not depend onmaximization solutions found for other hours).

In another example processing scenario, the forecast data 710 can beused to run different instances of the optimization problem to accountfor variability in data. As an example of this approach, the inner andouter loop processing may be conducted such that only CPL opportunitiesin the forecast interval are consider in the optimization. Then, eachiteration of the outer loop can correspond to a certain optimalpeak-fire capability. This can enable the generation of an incrementalcost curve as a function of peak-fire hours or MWHr.

Embodiments of the dispatch optimization system 402 described herein canserve as a tool for long-term planning of plant asset operation byidentifying optimal peak-fire and CPL operation opportunities determinedto substantially maximize profit generated by one or more plant assetsover a maintenance interval. During real-time operation within themaintenance interval, the system 402 can assist with day-ahead andreal-time planning and operation of the assets by leveraging real-timeconditions and historical operation data to dynamically refine thelong-term plan, and to present meaningful information that guidesprofitable operation of the assets. In this way, the system helps plantmanagers and operators to utilize the full value potential of theirplant assets while satisfying target life requirements. The techniquesimplemented by the dispatch optimization system 402 allow thesesubstantially optimized operating profiles and metrics to be calculatedwith relatively low computational overhead despite long operatinghorizons over which the optimization problem must be run.

FIGS. 21-24 illustrate methodologies in accordance with one or moreembodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methodologies shown hereinare shown and described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance therewith, occur in a differentorder and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a methodology could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation. Furthermore, interactiondiagram(s) may represent methodologies, or methods, in accordance withthe subject disclosure when disparate entities enact disparate portionsof the methodologies. Further yet, two or more of the disclosed examplemethods can be implemented in combination with each other, to accomplishone or more features or advantages described herein.

FIG. 21 illustrates an example methodology 2100 for generating aprofit-maximizing operating schedule or profile for a plant asset.Initially, at 2102, an initial value of a price of life λ is set, wherethe price of life λ represents a monetary value of parts-life consumedby operation of a plant asset (e.g., one or more gas turbines or otherpower-generating plant assets). At 2104, based on forecasted electricityprices, forecasted fuel prices, performance model data for the plantasset, and parts-life model data for the plant asset, an hourlyoperating schedule for the plant asset is determined in terms of poweroutput and/or operating temperature (and/or other operating variables)that substantially maximizes, for each time unit of a maintenanceinterval, a profit value given by

[Electricity Revenue]−[Fuel Cost]−λ*[Parts-Life Consumed]

For scenarios in which the operating schedule is to be found in terms ofboth plant output MW and operating temperature T, the profit to bemaximized for each time unit can be given by equation (2) above.However, if the operating schedule is to be found in terms of othervariables, other suitable profit calculations formulas defined in termsof such other variables can be used. In all cases, the profitcalculation considers the cost of parts-life consumed (the product ofthe price of life value λ and the calculated amount of parts-lifeconsumed as a function of the operating variables). Step 2104, which canbe considered an inner loop of an overall iterative problem solvingprocess, may require multiple iterations to find the maximum profit foreach time unit of the maintenance interval, depending on the number ofoperating variables (e.g., power output MW, operating temperature T,etc.) that are to be defined in the operating schedule.

At 2106, an amount of parts-life that would be consumed as a result ofrunning the plant asset in accordance with the operating scheduleobtained in step 2104 is determined. The parts-life that would beconsumed can be determined, for example, based on parts-life model datafor the plant asset, which defines the estimated amount of parts-lifeconsumed by the asset as a function of power output MW and/or operatingtemperature T (or other operating variables).

At 2108 a determination is made as to whether the amount of consumedparts-life calculated at step 2106 is equal to a target life for theasset (within a defined tolerance). If the amount of parts-life does notequal the target life 1108 (NO at step 2108), the methodology proceedsto step 2112, where a determination is made as to whether the amount ofparts-life determined at step 2106 is less than the target life. If theamount of parts-life is less than the target life (YES at step 2112),the methodology proceeds to step 2114, where the price of life value λis increased. Alternatively, if the amount of parts-life is not lessthan the target life (NO at step 2112), the methodology proceeds to step2116, where the price of life value λ is increased. After eitherdecreasing or increasing the price of life value λ at steps 2114 or2116, respectively, the methodology returns to step 2104, and anotheroperating schedule is determined using the updated value of the price oflife λ. Steps 2106, 2108, 2112, 2114, and 2116 can be considered anouter loop of the overall iterative schedule determination process.

Steps 2104, 2106, 2108, 2112, 2114, and 2116 are repeated until adetermination is made at step 1208 that the amount of parts-life isequal to the target life (within the defined tolerance). When the amountof parts-life is equal to the target life (YES at step 2108), themethodology proceeds to step 2110, where the most recent operatingschedule determined at step 2104 is output. In some embodiments, theoperating schedule can be output as a report or otherwise rendered in aformat that can be reviewed by a user. Alternatively, in someembodiments the operating schedule can be exported to a plant assetcontrol system so that operation of the asset will be controlled inaccordance with the schedule.

FIG. 22 illustrates an example methodology 2200 for determiningparts-life credits and deficits relative to a target life for one ormore power-generating plant assets. Initially, at 2202, a price of lifevalue representing a monetary value of parts-life consumed by operationof a plant asset over a maintenance interval is determined. At 2204, anhourly operating schedule or profile for the plant asset is received.The operating schedule defines at least an hourly power output and anhourly operating temperature for the plant asset over at least a portionof a maintenance interval. In some embodiments, the price of life valueobtained at step 2202 and the hourly operating schedule obtained at step2204 can be derived using methodology 2100 described above (e.g.,through iterations of the inner and outer loop processing carried out bythe profile generation component 406). Alternatively, the operatingschedule can be a user-defined schedule provided by a user.

At 2206, an amount of parts-life that would be consumed by the plantasset as a result of operating the asset in accordance with theoperating schedule obtained at step 2204 is determined. This amount ofparts-life can be determined, for example, using parts-life model datafor the plant asset together with the hourly power output and operatingtemperatures defined by the operating schedule. At 2208, based on theestimated amount of parts-life determined at step 2206, a net amount ofparts-life that would be created or debited for the plant asset relativeto a target life of the maintenance interval is determined. For example,since CPL operation causes parts-life to be consumed more slowlyrelative to base load operation, CPL hours identified by the operatingschedule will create parts-life credits. Conversely, since peak-fireoperation causes parts-life to be consumed more quickly, peak-fire hoursidentified by the operating schedule will yield parts-life createparts-life deficits. The net of parts-life credits and deficits yieldsthe net amount of parts-life that will be credited or debited as aresult of operation in accordance with the operating schedule.

At 2210, a determination is made as to whether the net amount ofparts-life determined at step 2208 is greater than 0, indicating a netparts-life credit (or bank). If the net amount of parts-life is greaterthan 0 (YES at step 2210), the methodology proceeds to step 2212, where,based on the parts-life and performance models, the net amount ofparts-life is converted to a number of banked MWh that can be generatedduring peak-fire operation without causing operation of the plant assetto violate the target life of the maintenance interval. Alternatively,if the net amount of parts-life is not greater than 0 (NO at step 2210),the methodology moves to step 2214, where, based on the parts-life andperformance models, the net amount of parts-life is converted to anumber of MWh that are to be compensated for via cold part-loadingoperation in order to prevent violation of the target life of themaintenance interval. In some embodiments, the net amount of parts-lifecan be updated during operation based on actual operation data (e.g.,actual hourly values of power output MWh and operating temperature T) aswell as updated forecasted market and/or ambient data. This running netamount of parts-life can also be plotted graphically over time to showthe number of MWh available for peak-firing, or the number of MWh thatmust be compensated for by CPL operation in order to satisfy the targetlife constraint for the current maintenance interval.

FIG. 23 illustrates an example methodology 2300 for identifying suitableperiods during which to profitably peak-fire power-generating plantassets. Initially, at 2302, a price of life value representing amonetary value of parts-life consumed as a result of operating a plantasset over a maintenance interval is determined. In some embodiments,the price of life value can be derived using methodology 2100 describedabove (e.g., through iterations of the outer loop processing carried outby the profile generation component 406). At 2304, an hourly operatingschedule or profile for a plant asset is received. The operatingschedule defines at least an hourly power output and an hourly operatingtemperature for the plant asset over at least a portion of a maintenanceinterval, including the day ahead or the current day. In someembodiments, the hourly operating schedule obtained at step 2302 can bederived using methodology 2100 described above (e.g., through iterationsof the inner loop processing carried out by the profile generationcomponent 406) based on the price of life value determined at step 2302.Alternatively, the operating schedule can be a user-defined scheduleprovided by a user.

At 2306, during operation of the plant asset during the maintenanceinterval, the portion of the operating schedule corresponding to thecurrent day is updated based on current fuel and electricity prices,actual operation data for the plant asset (e.g., historical hourlyvalues of the power output and operating temperature for past hours ofthe maintenance interval), forecasted fuel and electricity prices forthe remaining hours of the maintenance interval, and the price of lifevalue determined at step 2302. For example, the inner loop processingdescribed above (e.g., step 2104 of methodology 2100) can be re-executedusing the current and historical data for the current day in place offorecasted data for past hours of the maintenance interval, and updatedforecast data for the remaining hours of the maintenance interval.

In some embodiments, the price of life value can be updated periodicallyvia step 2302, and the operating schedule can similarly be updatedperiodically via step 2306 but at a higher frequency than the updatingof the price of life value. For example, the price of life value may beupdated once a day, while the operating schedule may be updated everyhour or every few hours throughout the day to reflect updated forecastsor deviations in the operation of the plant asset from plannedoperation.

At 2308, a minimum electricity price at which selling peak-fired MWhwill yield a profit is determined based on the price of life valueobtained at step 2302. At 2310, one or more hours of the maintenanceinterval at which the forecasted electricity price is equal to orgreater than the minimum electricity price determined at step 2308 areidentified as a recommended peak-fire times. At 2312, the updatedoperating schedule and the recommended peak-fire times are rendered on auser interface.

FIG. 24 illustrates an example methodology 2400 for identifying suitableperiods during which to cold part-load power-generating plant assets ina cost-efficient manner. Initially, at 2402, an hourly operatingschedule for a plant asset is received that defines at least an hourlypower output and an hourly operating temperature for the plant assetover a maintenance interval. At 2404, a price of life value λ* isdetermined, where the price of life value represents a monetary value ofparts-life consumed by operation of the plant asset in accordance withthe operating schedule received at step 2402. In one or moreembodiments, the operating schedule and the price of life value λ* canbe determined using methodology 2100 (e.g., executed by profilegeneration component 406), based on forecasted electricity and fuelprices, asset performance model data, and asset parts-life model data.

At 2406, for respective hours of a current day of the maintenanceinterval, values of the operating temperature are determined thatmaximize or substantially maximize

[Electricity Revenue]−[Fuel Cost]−λ*[Parts-Life Consumed]

where at least one of the electricity revenue, the fuel cost, or theparts-life consumed are calculated using updated electricity and fuelprices, as well as actual power output values generated by the plantasset for prior hours of the current day.

At 2408, one or more of the respective hours of the current day areidentified as recommended cold part-load operating times based on thevalues of the operating temperature obtained at step 2406. For example,hours for which the operating temperatures obtained at step 2406 arebelow baseline (e.g., hot load) temperatures can be identified assuitable cold part-load hours. In some embodiments, the recommended coldpart-load operating hours may also be correlated with the forecastedfuel price information in order to identify a subset of the recommendedcold part-load hours corresponding to one or more lowest fuel prices.This subset of recommended hours may be flagged as hours that are bestsuited for cold part-load operation. At 2410, the recommended coldpart-load operation times are rendered on user interface. In someembodiments, operation of the plant asset may be automaticallycontrolled in accordance with the recommended cold part-load hours, suchthat the plant asset's operating temperature is automatically regulatedin accordance with the results of step 2408.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 25 and 26 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 25, an example environment 2510 for implementingvarious aspects of the aforementioned subject matter includes a computer2512. The computer 2512 includes a processing unit 2514, a system memory2516, and a system bus 2518. The system bus 2518 couples systemcomponents including, but not limited to, the system memory 2516 to theprocessing unit 2514. The processing unit 2514 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit2514.

The system bus 2518 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 2516 includes volatile memory 2520 and nonvolatilememory 2522. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer2512, such as during start-up, is stored in nonvolatile memory 2522. Byway of illustration, and not limitation, nonvolatile memory 2522 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 2520 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 2512 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 25 illustrates, forexample a disk storage 2524. Disk storage 2524 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 2524 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 2524 to the system bus 2518, a removableor non-removable interface is typically used such as interface 2526.

It is to be appreciated that FIG. 25 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 2510. Such software includes an operatingsystem 2528. Operating system 2528, which can be stored on disk storage2524, acts to control and allocate resources of the computer 2512.System applications 2530 take advantage of the management of resourcesby operating system 2528 through program modules 2532 and program data2534 stored either in system memory 2516 or on disk storage 2524. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 2512 throughinput device(s) 2536. Input devices 2536 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 2514through the system bus 2518 via interface port(s) 2538. Interfaceport(s) 2538 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 2540 usesome of the same type of ports as input device(s) 2536. Thus, forexample, a USB port may be used to provide input to computer 2512, andto output information from computer 2512 to an output device 2540.Output adapters 2542 are provided to illustrate that there are someoutput devices 2540 like monitors, speakers, and printers, among otheroutput devices 2540, which require special adapters. The output adapters2542 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 2540and the system bus 2518. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 2544.

Computer 2512 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)2544. The remote computer(s) 2544 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer2512. For purposes of brevity, only a memory storage device 2546 isillustrated with remote computer(s) 2544. Remote computer(s) 2544 islogically connected to computer 2512 through a network interface 2548and then physically connected via communication connection 2550. Networkinterface 2548 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 2550 refers to the hardware/softwareemployed to connect the network interface 2548 to the system bus 2518.While communication connection 2550 is shown for illustrative clarityinside computer 2512, it can also be external to computer 2512. Thehardware/software necessary for connection to the network interface 2548includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 26 is a schematic block diagram of a sample computing environment2600 with which the disclosed subject matter can interact. The samplecomputing environment 2600 includes one or more client(s) 2602. Theclient(s) 2602 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 2600also includes one or more server(s) 2604. The server(s) 2604 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 2604 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 2602 and servers 2604 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 2600 includes acommunication framework 2606 that can be employed to facilitatecommunications between the client(s) 2602 and the server(s) 2604. Theclient(s) 2602 are operably connected to one or more client datastore(s) 2608 that can be employed to store information local to theclient(s) 2602. Similarly, the server(s) 2604 are operably connected toone or more server data store(s) 2610 that can be employed to storeinformation local to the servers 2604.

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methodologieshere. One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A method, comprising: receiving, by a systemcomprising at least one processor, operating profile data for one ormore power-generating assets defining values of one or more operatingvariables for respective time units of a life cycle; determining, by thesystem for a first subset of the respective time units of the life cyclecorresponding to a first operating mode that produces parts-life creditsrelative to a target life, an amount of parts-life credited relative tothe target life based on the operating profile data and parts-life modeldata for the one or more power-generating assets; determining, by thesystem for a second subset of the respective time units of the lifecycle corresponding to a second operating mode that consumes theparts-life credits relative to the target life, an amount of parts-lifeconsumed relative to the target life based on the operating profile dataand the parts-life model data; determining, by the system, an amount ofbanked parts-life at a current time of the life cycle based on a net ofthe amount of parts-life credited and the amount of parts-life consumed;converting, by the system, the amount of banked parts-life to an amountof available power output capable of being generated by the secondoperating mode during the life cycle without violating the target life;and rendering, by the system, the amount of available power output on aninterface display.
 2. The method of claim 1, further comprising:determining, by the system, a price of life value representing a costper unit of the banked parts-life for the one or more power-generatingassets, wherein the price of life value is one of a non-vector value ora vector value; determining, by the system based on the price of lifevalue, a minimum electricity price at which selling the available poweroutput will yield a profit; and rendering, by the system on theinterface display or another interface display, the minimum electricityprice.
 3. The method of claim 2, further comprising: identifying, by thesystem, one or more time units of the life cycle during which the secondoperating mode is recommended based on the minimum electricity price andforecasted electricity price data; and rendering, by the system on theinterface display or another interface display, the one or more timeunits during which the the second operating mode is recommended.
 4. Themethod of claim 1, further comprising plotting, by the system on theinterface display or another interface display, a cumulative value ofthe amount of available power output over time.
 5. The method of claim1, further comprising: determining, by the system, a price of life valuerepresenting a cost per unit of the banked parts-life for the one ormore power-generating assets; identifying, by the system, one or moretime units of the life cycle during which the first operating mode isrecommended based on the price of life value, forecasted electricityprice data, forecasted fuel price data, performance model data thatmodels fuel consumption for the one or more power-generating assets, andparts-life model data that models parts-life consumption for the one ormore power-generating assets; and rendering, by the system on theinterface display or another interface display, the one or more timeunits during which the first operating mode is recommended.
 6. Themethod of claim 5, wherein the identifying the one or more time unitsduring which the first operating mode is recommended comprisesidentifying, for respective time units of the life cycle, an operatingtemperature T for the one or more power-generating assets that maximizesor substantially maximizesElectricityPrice*MW−FuelCost*FuelUsed(MW,T,Amb)−λ*FHH_Consumed(MW,T,Amb)where ElectricityPrice is a forecasted or actual price of power at thetime unit, MW is a forecasted or actual value of the power output forthe time unit, FuelCost is a forecasted or actual price of fuel for thetime unit, Amb is one or more values of one or more ambient conditionsfor the time unit, FuelUsed(MW, T, Amb) is a forecasted amount of fuelconsumed for the time unit as a function of MW, T, and Amb, λ is theprice of life value, and FHH_Consumed(MW, T, Amb) is a forecasted amountof parts-life created or consumed for the time unit as a function of MW,T, and Amb.
 7. The method of claim 6, further comprising controlling, bythe system, operation of the one or more power-generating assets inaccordance with values of the operating temperature T determined for therespective time units.
 8. The method of claim 6, further comprising:updating, by the system, the price of life value on a periodic basisbased on historical operation data for the one or more plant assets forpast time units of the life cycle, forecasted electricity price data andgas cost data for remaining time units of the life cycle, and forecastedambient data for the remaining time units of the life cycle to yield anupdated price of life value; and updating, by the system for therespective time units, the operating temperature T multiple times duringa day of the life cycle based on the updated price of life value.
 9. Themethod of claim 1, wherein the receiving the operating profile datacomprises: selecting, by the system, a price of life value representinga cost per unit of banked parts-life for the one or morepower-generating plant assets; determining, by the system for respectivetime units of the life cycle, provisional values of the one or moreoperating variables that maximize or substantially maximize profitvalues based on the price of life value, the one or more operatingvariables comprising at least one of power output or operatingtemperature; determining, by the system, a predicted amount of consumedparts-life over the life cycle for the one or more power-generatingassets based on the provisional values of the one or more operatingvariables; and in response to determining that the predicted amount ofconsumed parts-life does not violate the target life, generating, by thesystem, the operating profile data based on the provisional values ofthe one or more operating variables.
 10. A system, comprising: a memorythat stores executable components; a processor, operatively coupled tothe memory, that executes the executable components, the executablecomponents comprising: a profile generation component configured togenerate operating profile data for the one or more power-generatingassets, wherein the operating profile data comprises values of one ormore operating variables for respective time units of a maintenanceinterval; a parts-life metric component configured to determine, for afirst subset of the respective time units of the maintenance intervalcorresponding to a first mode of operation that produces parts-lifecredits relative to a target life based on the operating profile dataand parts-life model data for the one or more power-generating assets, anumber of parts-life credits generated by the first mode of operation,wherein the parts-life credits represent an amount of parts-life thatcan be consumed by a second mode of operation that consumes theparts-life credits during the maintenance interval without violating aconstraint relative to a target life, determine, for a second subset ofthe respective time units of the maintenance interval corresponding tothe second mode of operation based on the operating profile data and theparts-life model data, a number of parts-life debits generated by thesecond mode of operation, wherein the parts-life debits represent anamount of parts-life to be compensated for by the first mode ofoperation during the maintenance interval to prevent violation of theconstraint relative to the target life, determine an amount of bankedparts-life at a current time of the maintenance interval based on abalance of the number of parts-life credits and the number of parts-lifedebits, and convert the amount of banked parts-life to an amount ofbanked power output available for the second mode of operation duringthe maintenance interval that will not violate the constraint relativeto the target life; and a user interface component configured to renderthe amount of banked power output available for the second mode ofoperation on an interface display.
 11. The system of claim 10, whereinthe profile generation component is further configured to determine aprice of life value representing a cost per unit of the bankedparts-life for the one or more power-generating assets, the price oflife value being one of a non-vector value or a vector value, theparts-life metric component is further configured to determine a minimumelectricity price or a range of electricity prices at which selling thebanked power output will yield a profit, and the user interfacecomponent is further configured to render the minimum electricity priceor the range of electricity prices on the interface display or anotherinterface display.
 12. The system of claim 11, wherein the profilegeneration component is further configured to identify one or more timeunits of the maintenance profile during which the second mode ofoperation is recommended based on the minimum electricity price andpredicted electricity price data, and the user interface component isfurther configured to render, on the interface display or anotherinterface display, one or more graphical indications identifying the oneor more time units during which the second mode of operation isrecommended.
 13. The system of claim 10, wherein the profile generationcomponent is further configured to determine a price of life valuerepresenting a cost per unit of the banked parts-life for the one ormore power-generating assets, and identify one or more time units of themaintenance profile during which the first mode of operation isrecommended based on the price of life value, predicted electricityprice data, predicted fuel price data, performance model data thatmodels fuel consumption for the one or more power-generating assets, andparts-life model data that models parts-life consumption for the one ormore power-generating assets, and the user interface component isfurther configured to render, on the interface display or anotherinterface display, one or more graphical indications identifying the oneor more time units during which the first mode of operation isrecommended.
 14. The system of claim 13, wherein the profile generationcomponent is configured to identify the one or more time units duringwhich the first mode of operation is recommended by identifying, forrespective time units of the maintenance interval, an operatingtemperature T for the one or more power-generating assets that maximizesor substantially maximizesElectricityPrice*MW−FuelCost*FuelUsed(MW,T,Amb)−λ*FHH_Consumed(MW,T,Amb)where ElectricityPrice is a forecasted or actual price of power at thetime unit, MW is a forecasted or actual value of the power output forthe time unit, FuelCost is a forecasted or actual price of fuel for thetime unit, Amb is one or more values of one or more ambient conditionsover time, FuelUsed(MW, T, Amb) is a forecasted amount of fuel consumedfor the time unit as a function of MW, T, and Amb, λ is the price oflife value, and FHH_Consumed(MW, T, Amb) is a forecasted amount ofparts-life created or consumed for the time unit as a function of MW, T,and Amb.
 15. The system of claim 14, further comprising a controlinterface component configured to control operation of the one or morepower-generating assets in accordance with values of the operatingtemperature T determined for the respective time units.
 16. The systemof claim 14, wherein the profile generation component is furtherconfigured to update the price of life value on a periodic basis basedon historical operation data for the one or more power-generating assetsfor past time units of the maintenance interval, predicted electricityprice data for remaining time units of the maintenance interval, andpredicted ambient data for the remaining time units of the maintenanceinterval to yield an updated price of life value, and update, for therespective time units, the operating temperature T multiple times duringa day of the maintenance interval based on the updated price of lifevalue.
 17. The system of claim 10, wherein the profile generationcomponent is configured to: select a price of life value representing acost per unit of banked parts-life for the one or more power-generatingassets; determine, for respective time units of the life cycle,provisional values of the one or more operating variables that maximizeor substantially maximize profit values based on the price of lifevalue, wherein the one or more operating variables comprise at least oneof power output or operating temperature; determine a predicted amountof consumed parts-life over the maintenance interval for the one or morepower-generating assets based on the provisional values of the one ormore operating variables; and in response to determining that thepredicted amount of consumed parts-life does not violate the constraintrelative to the target life, generate the operating profile data basedon the provisional values of the one or more operating variables.
 18. Anon-transitory computer-readable medium having stored thereon executableinstructions that, in response to execution, cause a system comprisingat least one processor to perform operations, the operations comprising:receiving operating profile data for one or more power-generating assetsdefining values of one or more operating variables for respective timeunits of a maintenance interval; determining, for a first subset of therespective time units of the maintenance interval corresponding to afirst operating mode that credits parts-life relative to a target lifefor the one or more power-generating assets, an amount of parts-lifecredited relative to the target life based on the operating profile dataand parts-life model data for the one or more power-generating assets;determining, for a second subset of the respective time units of themaintenance interval corresponding to a second operating mode thatconsumes parts-life relative to the target life for the one or morepower-generating assets, an amount of parts-life consumed relative tothe target life based on the operating profile data and the parts-lifemodel data; determining an amount of banked parts-life at a current timeof the maintenance interval based on a net of the amount of parts-lifecredited and the amount of parts-life consumed; determining an amount ofavailable power output capable of being generated by the secondoperating mode during a remainder of the maintenance interval withoutviolating the target life as a function of the amount of bankedparts-life; and displaying the amount of available power output on aninterface display.
 19. The non-transitory computer-readable medium ofclaim 18, wherein the operations further comprise: determining a priceof life value representing a cost per unit of the banked parts-life forthe one or more power-generating assets, wherein the price of life valueis one of a non-vector value or a vector value; determining based on theprice of life value, a minimum electricity price or a range ofelectricity prices at which selling the available power output willyield a profit; and displaying, on the interface display or anotherinterface display, the minimum electricity price or the range ofelectricity prices.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the operations further comprise: identifying one ormore time units of the maintenance profile during which the secondoperating mode is recommended based on the minimum electricity price andforecast electricity price data; and displaying, on the interfacedisplay or another interface display, the one or more time units duringwhich the first operating mode is recommended.